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The development of an effective hydraulic unit (HU) diagnostic system, which recognizes critical fault at an early stage of their development, is a relevant and practically important objective for many hydroelectric power plants (HPP). The main trends in recent years in this area are the expansion of the hardware base and the introduction of digital technologies to handle the increasing flow of data monitoring. At the same time, much less attention is paid to the improvement of algorithms for potential defect recognition and the creation of new diagnostic rules; much of the monitoring data are used only for the trend construction, and do not participate in the diagnosis. The method of vector diagrams is proposed for estimating actual forces applied towards the support joints of an HU: each mode of operation is represented on a phase plane in the form of a vector proportional to the vibration displacement. The vector difference determines the actual forces of mechanical, electromagnetic and hydraulic origin in each operating mode, taking into account the individual characteristics of the HU. The example of vector diagram analysis shows how to obtain more useful information about the technical status of the unit from the same amount of basic vibration monitoring data, to predict possible deterioration in the operation of the equipment well before the generation of a warning or an alarm signal by the diagnostic system.

Wind energy is one of the most relevant clean energies today, so wind turbines must have good health and be reliable in operation. Current wind turbines have slender and elastic structures that can be easily damaged through vibrations and compromise their health; therefore, vibration monitoring is essential to ensure safe operation. Here, we present a method for simple wind turbine vibration monitoring in the laboratory by means of an accelerometer placed on a weathervane under different scenarios, with recording of different amplitudes of vibrations caused at a constant speed of 10 km/h. The variables, trends, and data captured during vibration monitoring were then used to implement a prediction system of synthetic failure using machine learning methods such as: Medium Trees, Cubic SVN, Logistic Regression Kernel, Optimized Neural Network, and Bagged Trees, with the last demonstrating an accuracy of up to 0.87%.

During the fire which took place on 15th April 2019, the Cathedral of Notre Dame de Paris underwent extensive structural damages: most of the roof was destroyed, part of the stone vaulted ceiling collapsed, and the wooden framework of the north tower was partially burned. After the fire, a list of actions was carried on in order to secure the remaining structure, prevent any further damage and start the rehabilitation. Very early on, a monitoring system was used to assist the project manager in these tasks. With the progress of site works, the monitoring system was updated to provide more insightful information. The present paper focuses on the monitoring system deployed on the towers and the stone vaulted ceiling with the objective to produce a first assessment of the current state and follow the evolution during the reconstruction phase. The monitoring campaign was carried out from December 2020 to December 2021 with two distinct sets of sensors. The first objective was to characterize the current state and identify the dynamic behavior of the structure, for that purpose 48 directional accelerometers were installed on the two towers and the two vaults lane. Acceleration measurements and analytical methods lead to the identification of the fundamental modes of these structural elements. In a second time, to follow the evolution of the vaults, 56 inclinometers were positioned on the outer side of the vaults, constituting 15 inclinometer measuring chains. Set up to transmit data continuously with a 10 min frequency, measurements allow to notify settlements or uprisings and correlate these evolutions with external factors (weather conditions or rehabilitation works).

In order to study the applicability of high-pressure gas expansion rock-cracking technology in hard rock tunnel near historic sites, theoretical analysis, field tests as well as vibration monitoring are conducted to obtain suitable rock mass cracking parameters for tunnel excavation. The results show that the ideal effect of rock mass cracking can be achieved with the cutting mode of “central vertical empty hole + double wedge cutting hole” and the auxiliary hole network parameter of “0.8 m × 0.7 m”. The measured vibration velocity is less than 0.1 cm/s at the monitoring point 60 m away from the tunnel face in the field test, which meets the vibration control requirements of the historic sites in the process of tunnel excavation. The research results show that as long as there is a high quality of hole plugging and no punching, the high-pressure gas expansion rock-cracking technology has the advantages of little vibration, low noise and less flying rocks, which provides a technical reference for the excavation of hard rock tunnels near ancient buildings and historic sites.

Рассматриваются вопросы совместного применения различных методов диагностирования для оценки технического состояния функциональных узлов асинхронных электродвигателей. Оценивается возможность применения комплекса методов вибродиагностики и спектрального анализа тока статора и внешнего магнитного поля в пусковом и установившемся режимах работы асинхронных электродвигателей. Исследования проведены на реальном высоковольтном асинхронном электродвигателе дымососа одной из тепловых электрических станций. В ходе исследования выполнен поиск механических дефектов, динамического эксцентриситета и повреждений обмотки ротора. Проанализированы результаты проведённых исследований и подтверждена эффективность и целесообразность совместного применения методов вибродиагностики и спектрального анализа сигналов тока статора и внешнего магнитного поля для выявления повреждений обмотки ротора и механических узлов асинхронного электродвигателя, The issues of combined application of various diagnostic methods for condition assessment of the functional units of induction motors are considered. The possibility of using a methods set of vibration monitoring and signature analysis of the stator current and external magnetic field in the start-up and steady-state modes of induction motors is assessed. The studies were carried out on a real high-voltage induction motor of an induced-draft fan of one of the thermal power plants. During the study, troubleshooting was made for mechanical failures, dynamic eccentricity and faults of the rotor winding. The results of the carried out studies are analyzed and the effectiveness and expediency of the combined application of vibration monitoring and signature analysis of stator current and external magnetic field signals methods to detect failures to the rotor winding and mechanical components of the induction motors are confirmed, Электрические станции, Выпуск 4 (1101) 2023, Pages 23-30

Beran Instruments Limited have been monitoring induction slip motors for over 20 years in high electrical noise environments. Due to the high-temperature environment of certain customer installations, integrated electronics piezo-electric (IEPE) sensors are not employed, and charge sensors are installed to monitor the condition of the motors. Over the past 20 years, Beran have used a variety of techniques, including the development of specialist isolated analogue signal conditioning amplifiers, to enable the true vibration signals that occur around the drive frequency (which is coupled to, and therefore affected by, the mains frequency) to be visualised.Beran Instruments Limited undertook a research and development programme, Machinery Health State Detection (mSTATE) supporting Future Net Zero Energy Generation. The goal of the programme was to develop a new capability to subtract electrical noise from sensor signals and was conducted in partnership with the University of Bristol and EDF Energy Torness Nuclear Power Station, with funding from the United Kingdom’s innovation agency, Innovate UK. The mSTATE project delivered a novel algorithm that allows complete subtraction of 50 Hz mains frequencies from the charge sensor signals allowing, for the first time, our end users, and future customers the ability to visualise the true vibration signals and the ability to trend machine health over time without the use of specialist and expensive analogue signal conditioning systems.This paper outlines the history and development of condition monitoring of induction slip motors at Beran Instruments, and how the mSTATE project resulted in a new approach to subtract 50 Hz noise from accelerometer signals using the power of modern signal processing and data architectures.

Statnamic Load Test (STN) is an economical alternative to a Static Load Test (SLT) for the determination of bearing capacity of piles. The shock impulse from STN test of about 150-250ms will induce vibration in the ground (in terms of Peak Particle Velocity, PPV). This vibration will then be damped and attenuated radially with distance from the test pile location. Lack of a reliable method in assessing the risk of ground vibration over distance limits the application of STN, especially if there is sensitive structures nearby. Middendrop (2011) and Chew et al. (2012) showed that PPV is proportional to the test load based on the compilation of a few sets of actual measured data, with test load up to about 16 MN, in Europe and Malaysia/Singapore respectively. However, over the last 10 years, there are many more number of STN tests conducted in Malaysia and Singapore with ground vibration measured, and at much higher test load level. It was observed that the maximum vibration induced by a STN test at much higher test load did not increase proportionally, but is seems to be capped at a maximum threshold value. It is noted that the piles in Malaysia and Singapore usually are terminated at competence soil (SPT’N >100) or hard rock. The data also suggest that the pile penetration length seems to have significant influence on the ground vibration. Massarch & Fellenius (2008) proposed a model to predict ground vibration induced by a hard driving of pre-cast pile, taking into account the effect of pile length. This paper aims to present a modified model to predict the ground vibration induced by a STN test at a higher test load (till 40 MN), with pile length effect included. A comparison between predicted and field measured PPV from a number of STN project sites is presented in this paper. The comparison shows a very good agreement between the measured and predicted PPV

Zaznavanje obrabe orodij je sodoben pereč problem v strojništvu. Zato se vse več napora vlaga v optimizacijo in avtomatizacijo odrezovalnih in preoblikovalnih procesov. Povečujejo se tudi potrebe po nadzoru delovanja obdelovalnih strojev, zato je pravočasno analiziranje in odpravljanje obrabljenosti orodja še kako pomembno. Posledično je v proizvodnjih procesih manj zastojev in je tako več časa namenjenega sami proizvodnji in manj odpravljanju nastalih napak. V okviru zaključne naloge smo tako razvili industrijski merilni sistem za spremljanje vibracij vretena frezalnega stroja in naredili časovno, ter frekvenčno analizo s pomočjo programa Excel in Matlab. Tool wear detection is a modern pressing problem in mechanical engineering. Therefore, more and more effort is being invested in the optimization and automation of cutting and reshaping processes. The need to monitor the operation of machine tools is also increasing, which is why time analysis and elimination of tool wear is even more important. As a result, there are fewer delays in production processes and more time is devoted to production itself and less time is spent on eliminating errors. As part of the final assignment, we thus developed an industrial measurement system for monitoring the vibrations of the spindle of a milling machine and made a time and frequency analysis using Excel and Matlab.

Double-shield TBM is more efficient than shield tunnel construction in hard rock strata. It is widely used in subway tunnel construction, such as in Qingdao and Chongqing in China. However, construction vibration problems caused by double-shield TBM critically affect human comfort in surface buildings. In order to study the vibration influence law of double-shield TBM, the on-site monitoring plan of double-shield TBM is developed, relying on a tunnel section of Qingdao Metro Line 4. Then, the vibration monitoring results are analyzed, and the time domain and frequency domain characteristics of vibration acceleration are obtained. Subsequently, taking vibration peak acceleration as the research object, the transverse and longitudinal vibration laws of double-shield TBM construction are studied. Finally, according to the standard of human comfort in buildings, the vibration influence ranges of double-shield TBM construction are obtained. The research results can provide a reference for the vibration effect of double-shield TBM passing through houses, highways and bridges. Moreover, it can also provide a basis for the disputes that double-shield TBM tunnel construction affects the lives of inhabitants.

The aim of the work is to create a computational and experimental method for the operational evaluation of the residual life of spur gears of technically complex products in the inter-repair period by conducting periodic vibration control. The proposed method is based on the main provisions of the theory of vibration-pulse diagnostics of gears developed in the Joint Institute of Mechanical Engineering of the National Academy of Sciences of Belarus. At the same time, the current value of the KV coefficient, which takes into account the internal dynamic load in the engagement of each gear, and its change during operation is determined based on the analysis of the time realizations of vibration accelerations. The main operation capacity criteria are the contact and bending strength of the teeth. An example of estimating the residual life of a gear pair is given that limits the reliability of a double-row planetary gear motor-wheel of a heavy-duty dump truck. The proposed method makes it possible at any stage of operation of a gear mechanism to assess the residual life of its gears, identify its most “weak” links, determine the probability of failure of the object, minimize the risk of accidents, optimize maintenance schedules, increase the inter-repair time. Thus, it is possible to organize highly effective monitoring of the reliability-limiting components of the technically complex products during operation and transition from planned preventive maintenance of machines to maintenance according to the actual condition.

Active and passive vibration control systems are of paramount importance in many engineering applications. If an external load excites a structure’s resonance and the damping is too low, detrimental events, such as crack initiation, growth and, in the worst case, fatigue failure, can be entailed. Damping systems can be commonly found in applications such as industrial machines, vehicles, buildings, turbomachinery blades, and so forth. Active control systems usually achieve higher damping effectiveness than passive ones, but they need a sensor to detect the working conditions that require damping system activation. Recently, the development of such systems in rotating structures has received considerable interest among designers. As a result, the development of vibration monitoring equipment in rotating structures is also a topic of particular interest. In this respect, a reliable, inexpensive and wireless monitoring system is of utmost importance. Typically, optical systems are used to measure vibrations, but they are expensive and require rather complex processing algorithms. In this paper, a wireless system based on a commercial MEMS accelerometer is developed for rotating blade vibration monitoring. The proposed system measurement accuracy was assessed by means of comparison with a reference wired measurement setup based on a mini integrated circuit piezoelectric (ICP) accelerometer adapted for data acquisition in a rotating frame. Both the accelerometers were mounted on the tip of the blade and, in order to test the structure under different conditions, the first four blade resonances were excited by means of piezoelectric actuators, embedded in a novel experimental setup. The frequency and amplitude of acceleration, simultaneously measured by the reference and MEMS sensors, were compared with each other in order to investigate the viability and accuracy of the proposed wireless monitoring system. The rotor angular speed was varied from 0 to 300 rpm, and the data acquisitions were repeated six times for each considered condition. The outcomes reveal that the wireless measurement system may be successfully used for vibration monitoring in rotating blades.

The increase in railway coverage significantly increased the railway mileage in karst areas. However, the fragile ecosystem in karst areas and weak mechanical performance in cavities cause hidden railway safety hazards. Even with the crucial stability and safety of railways in karst areas, there is only limited research conducted on the dynamic response of railway subgrade and the influence of hidden soil holes on the stability under different conditions. In this study, the Guizhou section of the Shanghai–Kunming high-speed railway was chosen as the research area. On-site monitoring and simulations using ABAQUS were performed to study the dynamic response of the subgrade under the dynamic load of a train. The influence of the soil hole, axle load, and vehicle speed on the dynamic response law and stability of the subgrade were considered based on the model rationality verification. Previous studies have demonstrated the considerable impact of soil holes on the dynamic response characteristics and stability of roadbeds. The ambient vibration frequency of the train was concentrated at 10–60 Hz. Under train load, the amplitude of the dynamic response parameters of each subgrade structural layer increased with the axle load; however, the increase in speed exhibited a greater impact on the vertical dynamic displacement and dynamic stress than on the dynamic acceleration, which affected the stability of the soil hole and roadbed. This study summarized the dynamic response characteristics of subgrade in karst areas under different conditions, thereby providing a design reference for high-speed railways in karst areas.

Introduction. The author proposes a new universal optimization approach to mechanical safety monitoring of buildings and structures in the process of their operation. The new approach involves periodic monitoring of mechanic vibrations. Materials and methods. The methodology of periodic monitoring performable within the framework of general monitoring is based on the analysis of changes in the first natural frequency of a building or a structure and the logarithmic decrement of its damping along three mutually perpendicular axes. The shape and position of resonance peaks of the amplitude spectrum are employed to analyze resonant frequencies and logarithmic decrements of damping. The analysis of those electronically archived parameters is performed, whose values change over time proportionately with vibrations of a monitored facility. The time dependence of the first resonant frequency of a structure is considered as a generalized characteristic of its mechanical safety. Results. The author has shown that findings of the spectral analysis performed in respect of the vibration process are irrespective of a measurement method, provided that the uniformity of external influences is ensured. The frequency dependence of the structure’s first mode of vibrations on time (per year) has two non-linear sections within the entire term of operation. Following the analysis of measurements taken at construction facilities exposed to wind loads, it’s been found that advanced monitoring instruments can identify changes in the value of the first resonant frequency and the damping decrement with a reasonable degree of accuracy. The author believes that this finding has great practical importance. Conclusions. Identification of a trend towards a change in frequency and damping decrement values for the first mode of natural vibrations can help to schedule actions for the improvement of the mechanical safety of an engineering structure, and in some cases it can prevent an upcoming accident.

Industry 4.0 relies on the adoption of digital technologies to gather data in real time and to analyse it, providing useful information to the manufacturing system. In this paper, what solutions modern production plants that are aspiring towards compliance with philosophy of the Industry 4.0 have to adopt, monitor and analyse the vibration data of the manufacturing systems using existing process and tool monitoring solutions. In addition, detailed explanation of vibration level reading in order to increase the protection of the production sources (machines, devices etc.) against human errors and malfunctions in terms of Total Quality Management (TQM) and Total Productive Maintenance (TPM) with the concept’s levels TPM1(operator level) and TPM2(periodic conditional reviews etc.) will be giving with a Montronix system’s integration on a CNC milling machine. Besides, optimization and monitoring function of production process will be demonstrated with related graphs and tables with values of different scenarios.

Gearboxes are frequently used in the mining industry, especially for power transmission between the electric drive and the ball mill; besides the extreme complexity of a ball mill gear transmission system, the fault diagnosis by vibration analysis can be easily distorted by the presence of impulsive noises due to the ball pulses on the mill shell. Although several works in the literature are related to the influence of an impulsive noise on the accuracy of the diagnosis, no dynamic model exists yet in the literature that can explain the influence of these forces on the dynamic behavior of gearboxes. This paper presents a new approach to determine the influence of the grinding forces in crack defects diagnosis. This approach is based on a hybrid numerical model of a 24-degree-of-freedom gearbox, simulating one gear train and two drive shafts. The impact forces of the mill drum are modelled by a discrete element method (DEM). The ball-filling rate (Fr), the mill speed (Nr), and the ball size (Db) are considered to study this phenomenon. The simulations results show by a time series representation, fast Fourier transform, and short-time Fourier transform (STFT), that the acceleration is significantly affected by the presence of the grinding forces, developing an impulsive noise due to the impact of the balls governed by the studied parameters.

Staranje vodnih pregrad je eden ključnih izzivov pregradnega inženirstva v Sloveniji kot tudi drugod po svetu. Poleg tega so pregrade izpostavljene spremembam v okolju (podnebne spremembe) in drugim časovno odvisnim vplivom, kot na primer spremembam obratovalnih režimov na pregradah, ki so primarno namenjene hidroenergetski izrabi. Skupek vseh teh sprememb dodatno prispeva k procesu staranja ter k zmanjšanju obratovalne varnosti objektov. Pregrade so zelo pomembni infrastrukturni objekti, ki prinašajo številne koristi kot tudi dodatno tveganje v okolju. V primeru tehničnih okvar in porušitev (delnih ali popolnih) lahko predstavljajo nevarnost za dolvodna območja. Zagotavljanje dobrega kondicijskega stanja starajočih se pregrad trenutno predstavlja enega glavnih izzivov pregradnega inženirstva, saj je povprečna starost slovenskih pregrad že več kot 40 let. S podobnimi izzivi se soočajo tudi drugod po svetu. V prispevku predstavljamo metodologijo za spremljanje kondicijskega stanja betonskih pregrad s spremljanjem vibracij. Metodologija temelji na uporabi nekontaktnih in kontaktnih meritev z beleženjem vibracij na površini konstrukcije. Eksperimentalno delo smo izvajali na pregradi Brežice, ki smo jo začeli spremljati že med gradnjo in nato v prvem letu obratovanja. The ageing of dams is one of the major challenges in specifically Slovenian and generally global dam engineering. Dams are exposed to environmental (climate) changes, as well as time-dependent effects, such as changes in the operating schedules of dams intended primarily for hydroelectric production. These changes can accelerate dams’ ageing and lead to a decrease in their structural and operational safety. Dams are an important part of the infrastructure, as they bring about numerous benefits and at the same time they are also sources of risk. For example, in the event of partial or total failure they pose significant risk to downstream areas. Aging of dams, preserving their functionality, and maintaining their structural health are currently the main challenges of dam engineering. The mean age of Slovenian dams is already over 40 years, although Slovenia is not unique in this situation. In this paper there is presented a novel methodology to monitor structural health of concrete dams, with the use of noncontact and contact measurements through observation of structural vibrations. We present the in-situ experiment on the Brežice dam that began during the dam’s construction and continued into the first year of its operation.

Tento článek se zabývá popisem metody měření vibrací lopatek axiálního ventilátoru s proměnným natočením lopatek metodou Blade Tip Timingu. Účinnost axiálních ventilátorů je řízena natočením jeho rozváděcích lopatek. Analýza metodou BTT může vést k nesprávným výsledkům v případě, že je použit pouze jeden BTT senzor a není tak zohledněn aktuální úhle natočení lopatky. Pro získání korektních výsledků musí být použity alespoň dva BTT snímače. Navržená metoda monitorování vibrací lopatek, založená na použití více BTT snímačů, je popsána v článku. Vhodnost navrženého řešení je ověřena v rámci případové studie. This article deals with the BTT method of blade vibration measurement used for condition monitoring of variable pitch axial fan blades. The efficiency of axial fans with variable pitch is controlled by adjustment of blade angle. BTT analysis may lead to incorrect results when only one BTT sensor is used for blade TOA measurement without consideration of the current blade angle and at least two sensors must be used. The method of blade vibration monitoring based on multiple BTT sensors is described in this paper. The suitability of the designed method is verified by a case study.

Building structures are subject to various deformations caused by external and internal factors. Deformations are determined by various methods in the form of monitoring. It is very important to monitor the dynamic vibration response on bridge structures since these measurements allow us to identify any possible damage over time and take appropriate action. Our experiment, described in this article, is based on the use of non-contact methods, among which we used a geodetic instrument RTS (Robotic Total Station) and a seismograph to measure vibrations. The purpose and novelty of our work are reflected in the use of geodetic instruments to determine the dynamic response and synchronization of the obtained results. When using RTS technology, we increased data acquisition from 9 to 26 measurements per second. Comparative analysis of the measured signals was performed using FFT (Fast Fourier Transformation) and LSP (Lomb–Scargle Periodogram), based on LSSA (Least-Squares Spectral Analysis). The results showed us that when using the RTS geodetic instrument, it is possible to achieve frequency spectra comparable to those measured with a seismograph instrument. By increasing the number of measurements, the RTS method can be used to obtain more continuous data, which are essential for dynamic analyses.

Mediumfrequency fiber Bragg grating (FBG) acceleration sensors are used in important applications in mechanical, aerospace and weapon equipment, and have strict requirements in terms of resonance frequency and sensitivity. A novel medium-frequency accelerometer, based on fiber Bragg grating and flexible hinges, is proposed in this paper. The differential structure doubles the sensitivity of the sensor while avoiding temperature effects. The structure model and principle for the sensor are introduced, the sensor’s sensing characteristics are theoretically analyzed, and the structure parameters for the sensor are determined through numerical analysis. The sensing experiments show that the resonance frequency of the sensor is approximately 2800 Hz, the sensitivity is 21.8 pm/g in the flat frequency range of 50–1000 Hz, and the proposed sensor has a good temperature self-compensation function and lateral anti-interference capability.

Time synchronous averaging for the extraction of periodic waveforms is a rather common processing method for rotating machinery diagnosis. By synchronizing the signal to the rotational angle of the component of interest, e.g. by using a keyphasor reference signal, it is possible to perform the averaging in the angular domain, thus obtaining an angle-synchronous signal. Jittering of the reference signal affects the quality of the synchronous averaging process, resulting in attenuation and uncertain estimation of the extracted synchronous signal, especially in the high frequency band. In this paper, the effects of random uncertainty in the pulse arrival times of the reference signal on the synchronous averaging method are studied, with the objective of assessing the relevance of such a jitter error to the extracted waveform and the indicators derived for monitoring purposes. First, a unified framework for the computed order tracking method is presented, and then a model linking the statistics of the random jitter to the statistics of the waveform extracted through synchronous averaging in angle domain is developed. The theoretical model connects the random jitter distribution, the number of averaged periods and the ratio of the period of interest to the reference trigger period, to the distribution of the amplitudes of the synchronous frequency components in the synchronously averaged signal. Approximate analytical solutions are derived for cases of interest, allowing the prediction of the attenuation bias and variability of the extracted components amplitudes. The model is first verified against numerical simulations in order to assess consistency, and then parametric studies are presented. Experimental validation is performed on both an experimental and an operational data sets involving respectively a helicopter gearbox and a helicopter fleet.

The relevance of the work. This paper discusses some results of the analysis of methodological approaches to the development of a single diagnostic criterion suitable for performing an estimation of the actual state of electric machines and the development of predictive degradation models. The purpose of the work: to summarize the results of the analysis of the parameters of vibrations generated by the operation of electric machines of various designs, which will allow classifying some defects of this equipment by basic groups and formalizing diagnostic signs for ease of use when developing the code of the automated control algorithm for complex systems according to frequency sets of diagnostic signs; to show that, under the conditions of the planned repairs system still operating in the coal and mining industries of Kuzbass, priority should be given only to short-term predictive mathematical models, which make it possible to assess the probability of the occurrence of equipment failures in the near future; to develop an algorithm for creating a unified diagnostic criterion suitable for identifying and assessing the degree of danger of electrical defects of mining fleet. Methods of the study. The study substantiated the need to use the results of an integrated approach to the diagnosis of electric machines according to parameters of vibration generated during their work, while simultaneously using several diagnostic methodologies, including spectral analysis, analysis of high-frequency vibration envelope, wavelet decomposition, and running down analysis. It is shown that an integrated approach to the diagnosis of vibration parameters opens up broad opportunities for the timely diagnosis of defects in the electromechanical equipment of mining machines, including defects that are still in their infancy. Results of the work. The results of the studies confidently prove the fundamental possibility of creating a new relevant single diagnostic criterion for identifying electrical defects, which can be used as a basic element of the maintenance system for machines according to its actual technical condition. The application of the developed criterion will make it possible to increase the efficiency of the maintenance management of complex mechanical systems and predict changes in the actual state of electric machines operated in the coal and mining industries.

A condition-based maintenance approach may be used for planning the maintenance activities of textile machines with a satisfactory performance by developing maintenance decision-making support based on fuzzy logic and vibration monitoring. Since textile machines are systems with moving parts operating at relatively high-speed, vibration monitoring was used to indicate their failure development. At the same time, the characterization of the degradation phenomenon of textile machines involves some degree of uncertainty and vagueness. Within this context, a knowledge-based approach that employed fuzzy logic and vibration monitoring was developed. Deterioration symptoms do announce future failures of industrial machines, therefore building a maintenance decision-making support for scheduling maintenance actions of textile machines based on the estimation of their condition becomes a resourceful way to prevent their further deterioration.

Used in many industrial applications, centrifugal pumps have optimal operating criteria specified at design. These criteria may not be precisely adhered to during operation which will ultimately reduce the life of the asset. Operators would therefore benefit from anticipating how often the design point is deviated from and hence how much asset degradation results. For centrifugal pumps, a novel set of covariates were proposed in this paper which formally partition observed operating zones with an Empirical Bivariate Quantile Partitioned distribution. This captured the dependency relation between operating parameters across plant configurations to predict the component wear that results from particular settings. The effectiveness of this was demonstrated through an operational case study in civil nuclear generation feedwater pumps where corroboration with bearing movements provides an indicator of plant wear. Such a technique is envisaged to inform operators of optimal plant configuration from multiple possibilities in advance of undertaking them.

Tese de Doutoramento em Engenharia Civil, Hoje em dia, a gestão de um vasto acervo de estruturas complexas e infraestruturas, que se encontram próximo ou para lá do seu fim de vida útil, constitui um importante desafio para os países desenvolvidos. Neste contexto, a manutenção e a prevenção têm vindo a representar custos muito significativos, sendo necessário adotar estratégias de gestão com relação custo-benefício otimizada, mas que ainda se encontram em desenvolvimento. Com a Monitorização da Integridade de Estruturas (em inglês, Structural Health Monitoring, SHM) pretende-se garantir a identificação imediata de danos, de forma a permitir uma avaliação automatizada da integridade dos sistemas estruturais. O desenvolvimento desta área de investigação visa a obtenção de métodos adequados e fiáveis para detetar os danos o mais cedo possível, e para que estes sejam encarados de forma imediata, focada e económica. A deteção de danos pode ser formulada como um problema de classificação com uma única classe e pode ser tratada de forma eficaz por meio de ferramentas numéricas bioinspiradas, como o Algoritmo de Seleção Negativa (NSA). A presente tese tem como objetivo desenvolver e testar uma metodologia de deteção de danos baseada numa versão inovadora do NSA com geração determinística. A metodologia é composta por vários recursos numéricos, para ultrapassar as deficiências identificadas na revisão da literatura. A metodologia proposta é validada em análises numéricas e estudos de caso, considerando cenários de danos múltiplos e incrementais, e condições ambientais e operacionais variáveis. Todas as conclusões são baseadas na análise experimental e estatística da aplicação dos algoritmos desenvolvidos, procurando-se desenvolver uma comparação justa com técnicas alternativas. A metodologia proposta revela-se apropriada para a deteção de danos em estado inicial de desenvolvimento. Pode ser adaptada a diferentes tipos de estruturas e propriedades estruturais sensíveis à ocorrência de dano. É robusta em relação a fontes de incerteza como o ruído nos sinais adquiridos, ao erro induzido pela extração das propriedades ou à flutuação devida às condições ambientais variáveis. O seu desempenho é fortemente afetado pela configuração dos parâmetros do algoritmo. Deste modo, são apresentadas diferentes abordagens de configuração, bem como se recomendam valores ou intervalos para parametrização da metodologia. Em conclusão, a estratégia de deteção de danos baseada em NSA, que é validada no contexto da presente tese, é considerada eficaz e os resultados promissores recomendam mais pesquisas e novas aplicações., Nowadays, developed countries are challenged by the management of a wide estate inventory of complex existing structures and infrastructures, which are either close or beyond the end of their service life. Maintenance and prevention have become a significant item of expenditure, while cost-effective strategies are required but still under development. Structural Health Monitoring (SHM) aims at the prompt identification of damage in order to allow an automated health condition assessment of structural systems. The development of such a field of investigation shall provide suitable and reliable methods for detecting the damage outbreak at the earliest possible stage, thus for facing it in a quick, focused and economic way. To this end, damage detection can be formulated as a one-class classification problem and effectively addressed through bio-inspired numerical tools, as the Negative Selection Algorithm (NSA). This thesis aims at developing and testing a damage detection methodology based on an innovative version of NSA with deterministic generation. The methodology is composed of several numerical features to tackle the relevant shortcomings that emerged during the literature review and the pilot tests. The individual features and the global methodology are validated on numerical instances and field-testing case studies, considering multiple and increasing damage scenarios and varying environmental and operational conditions. All the conclusions drawn in the present work are based on experimental analyses of the algorithms, performed based on a proper statistical design. Additional attention is paid to provide a fair comparison with alternative existing techniques. The proposed methodology results suitable for early-stage damage detection. It can be adapted to different types of structures and damage-sensitive features. It might be suitable for sensor embedment, by performing the detection on the acquisition of a single sensor. It is independent of the type of monitoring tools or excitation. It is robust against sources of uncertainties as the noise in the signals, the error induced by feature extraction and the fluctuation in the monitored features due to varying environmental conditions. Its performance is, instead, largely affected by the algorithm parameter setting. Therefore, different suitable setting designs are presented together with recommended values or ranges. In conclusion, the damage detection strategy based on NSA, that is validated in the context of the present thesis, is deemed effective and the promising results foster more research and further applications., This work is financed by national funds through FCT - Foundation for Science and Technology, under grant agreement SFRH/BD/115188/2016. Moreover, it is partly financed by FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020.

Copyright: © 2021 by the authors. Machinery with several rotating and stationary components tends to produce non-stationary and random vibration signatures due to the fluctuations in the input loads and process defects due to long hours of operation. Traditional heuristics methods are suitable for the detection of fault signatures, however, they become more complicated when the level of uncertainty or randomness exceeds beyond control. A novel methodology to identify these fault signatures using optimal filtering of vibration data is proposed to eliminate any false alarms and is expected to provide a higher probability of correct diagnosis. In this paper, a detailed pipeline of the algorithms are presented along with the results of the investigation that was carried out. These investigations are performed using open-source vibration data published by the NASA prognostics centre. The performance of these algorithms are evaluated based on the ground truth results published by NASA researchers. Based on the performance of these algorithms several parameters are fine-tuned to ensure generalisation and reliable performance. UK’s innovation agency, Innovate UK under grant agreement number 104505.

Vibration-condition monitoring aims to detect bearing damages of rotating machinery’s incipient failures mainly through time–frequency methods because of their efficient analysis of nonstationary signals. However, by having failures with impulse behavior, short-term events have a tendency to be diluted under variable-speed conditions, while information on frequency changes tends to be lost. Here, we introduce an approach to highlighting bearing impulsive failures by measuring short-term spectral components to deal with variable-speed vibrations. The short-term estimator employs two sliding windows: a small one that measures the instantaneous amplitude level and tracks impulsive components and a large interval that evaluates the average background amplitude. Aiming to characterize cyclo-non-stationary processes with impulsive behavior, the emphasizing high-order-based estimator based on the principle of spectral entropy is introduced. For evaluation, both visual inspection and classifier performance are assessed, contrasting the spectral-entropy estimator with the widely used spectral-kurtosis approach for dealing with impulsive signals. The validation of short-time/-angle spectral analysis performed on three datasets at variable speed showed that the proposed spectral-entropy estimator is a promising indicator for emphasizing bearing failures with impulse behavior.

Hypergravity accelerators are a type of large machinery used for gravity training or medical research. A failure of such large equipment can be a serious problem in terms of safety or costs. This paper proposes a prediction model that can proactively prevent failures that may occur in a hypergravity accelerator. An experiment was conducted to evaluate the performance of the method proposed in this paper. A 4-channel accelerometer was attached to the bearing housing, which is a rotor, and time-amplitude data were obtained from the measured values by sampling. The method proposed in this paper was trained with transfer learning, a deep learning model that replaced the VGG19 model with a Fully Connected Layer (FCL) and Global Average Pooling (GAP) by converting the vibration signal into a short-time Fourier transform (STFT) or Mel-Frequency Cepstral Coefficients (MFCC) spectrogram and converting the input into a 2D image. As a result, the model proposed in this paper has seven times decreased trainable parameters of VGG19, and it is possible to quantify the severity while looking at the defect areas that cannot be seen with 1D.

The major goal of the pre-study is to identify and prepare the required information, knowledge, tools, modules, and technologies for a project within Smart Manufacturing in the spirit of the collaboration between Sweden and India.WP3 focus is: Identification of the technologies needed to be considered in Smart-Pro project as an Innovation action. This means that the input technologies that will be identified in the Pre-study project should be in about TRL 3-, and the demonstrated results of Smart-Pro should be in about TRL 6-7. Smart Pro project will be based on effective collaboration between universities/research institutes and industry.In WP3, we will develop a list of the tools, methods, modules and technologies (in the areas below) which are available at the members of the Pre-study consortium (and in industry in general), and necessary for developing Smart-Pro system.According to the project description, WP3 has two Tasks:• T3.1: Production-maintenance technologies needed for Smart-Pro.• T3.2: Communication and integration technologies needed for Smart-Pro.The tasks have been distributed between E-maintenance AB (Emaint), which the leader of the WP and the two tasks, Linnaeus University (LNU), Uppsala University (UU) and Stockholm University (SU). Vinnova funded project- Pre-Study: Info & technology needed for Cloud based smart sustainable integrated production-maintenance system 98 /

В данной статье приведены результаты разработки методики проведения вибрационного контроля с использованием корпоративной программы «Dream for Windows». This article presents the results of the development of a method for conducting vibration control using the corporate program « Dream for Windows».

Vibration monitoring provides a good-quality source of information about the health condition of machines, and it is often based on the use of accelerometers. This article focuses on the use of accelerometer sensors in fabricating a low-cost system for monitoring vibrations in agricultural machines, such as rotary tedders. The aim of the study is to provide useful data on equipment health for improving the durability of such machinery. The electronic prototype, based on the low-cost AVR microcontroller ATmega128 with 10-bit ADC performing a 12-bit measurement, is able to acquire data from an accelerometer weighing up to 10 g. Three sensors were exposed to low accelerations with the use of an exciter, and their static characteristics were presented. Standard experimental tests were used to evaluate the constructed machine monitoring system. The self-contained prototype system was calibrated in a laboratory test rig, and sinusoidal and multisinusoidal excitations were used. Measurements in time and frequency domains were carried out. The amplitude characteristic of the preformed system differed by no more than 15% within a frequency range of 10 Hz–10 kHz, compared to the AVM4000 commercial product. Finally, the system was experimentally tested to measure acceleration at three characteristic points in a rotational tedder, i.e., the solid grease gearbox, the drive shaft bearing and the main frame. The RMS amplitude values of the shaft vibrations on the bearing in relation to the change in the drive shaft speed of two tedders of the same type were evaluated and compared. Additionally, the parameters of kurtosis and crest factor were compared to ascertain the bearing condition.

Structural health monitoring (SHM) can extend the operation of bridges beyond their original life span, increase the safety between scheduled inspections, and allow for a prioritized inspection after extreme events. One of the major challenges is to assess which damages can or cannot be diagnosed (i.e., detected or localized), which is essential to evaluate the value of a SHM system before it is installed, and to optimize the sensor placement accordingly.This work develops a framework to predict the minimum detectable damage, i.e., the minimum change in local structural design parameters that can reliably be detected based on changes in global damage-sensitive features. The diagnosis is considered "reliable" if the probability of false alarms is low and the probability of detection is high. Equivalently, a damage is "detectable" if it is significant under consideration of typical uncertainties related to ambient excitation and measurement noise and empirical safety thresholds. The approach requires vibration data from the undamaged structure in combination with a numerical model, and is universally applicable to a wide range of structures and damage-sensitive features. Secondly, a method is proposed to analyze the minimum localizable damage. The results show that optimal localizability is a compromise between high localization resolution, high detectability, and few false localization alarms. Thirdly, a sensor placement strategy is devised that takes as input the desired minimum diagnosable damage and optimizes the sensor layout and the number of sensors accordingly. The method allows one to focus the global damage diagnosis on local structural components. Ultimately, the monitoring of prestressing forces and support displacements is incorporated into the diagnostic framework, so that they can be analyzed and distinguished from changes in material properties or cross-sectional values.Besides the performance evaluation, the framework is suitable for quality control of existing instrumentation on real structures. Therefore, self-validation strategies are implemented to verify the input parameters, to validate the theoretical assumptions, and to check its effectiveness based on non-invasive tests using extra masses. The proof of concept studies based on a laboratory steel beam and a cable-stayed bridge show promising results regarding the practical application of the theoretical contributions.; La surveillance de l’intégrité des structures (structural health monitoring, SHM) peut prolonger l'utilisation des ponts au-delà de leur durée de vie initialement prévue, accroître la sécurité entre les inspections programmées et permettre une inspection prioritaire après des événements extrêmes. L'un des principaux défis consiste à déterminer quels endommagements peuvent ou non être diagnostiqués (c'est-à-dire détectés ou localisés), ce qui est essentiel pour évaluer la valeur d'un système de SHM avant son installation et également en conséquence pour optimiser le placement des capteurs.Cette thèse développe un cadre pour prédire les endommagements détectables, c'est-à-dire le changement minimum dans des paramètres structurels locaux qui peut être détecté de manière fiable sur la base des caractéristiques calculées à partir des données vibratoires. Le diagnostic est considéré comme "fiable" si la probabilité de fausses alertes est faible et la probabilité de détection élevée. De même, un endommagement est "détectable" si le changement dans les caractéristiques est important compte tenu des incertitudes typiques liées à l'excitation ambiante, le bruit de mesure et les seuils de sécurité empiriques. L'approche requiert des données vibratoires de la structure intacte et un modèle numérique, et est universellement applicable à un large éventail de structures et de caractéristiques. Par la suite, une méthode est proposée pour analyser les endommagements localisables. Les résultats montrent que la localisation optimale est un compromis entre une haute résolution de localisation, une grande détectabilité et peu de fausses alarmes de localisation. Avec ces résultats, une stratégie de placement de capteurs est élaborée en tenant compte de la diagnosticabilité d’endommagements souhaitée et optimise le placement et le nombre de capteurs en conséquence. La méthode permet d’axer le diagnostic global d’endommagements sur les composantes structurelles locales. Enfin, le suivi des forces de précontrainte et des déplacements d’appui est intégré dans les méthodes de diagnostic, afin que cela puisse être analysé et distingué des changements de propriétés des composantes structurelles.Outre l'évaluation des performances, les méthodes développées conviennent au contrôle de la qualité de l’instrumentation existante sur des structures réelles. Par conséquent, des stratégies d'auto-validation sont mises en œuvre pour vérifier les paramètres d'entrée, valider les hypothèses théoriques et vérifier l’efficacité sur des tests non invasifs utilisant des masses supplémentaires. Les études de preuve de concept basées sur une poutre en laboratoire et un pont à haubans montrent des résultats prometteurs en ce qui concerne l'application pratique des contributions théoriques.

Structural health monitoring (SHM) can extend the operation of bridges beyond their original life span, increase the safety between scheduled inspections, and allow for a prioritized inspection after extreme events. One of the major challenges is to assess which damages can or cannot be diagnosed (i.e., detected or localized), which is essential to evaluate the value of a SHM system before it is installed, and to optimize the sensor placement accordingly.This work develops a framework to predict the minimum detectable damage, i.e., the minimum change in local structural design parameters that can reliably be detected based on changes in global damage-sensitive features. The diagnosis is considered "reliable" if the probability of false alarms is low and the probability of detection is high. Equivalently, a damage is "detectable" if it is significant under consideration of typical uncertainties related to ambient excitation and measurement noise and empirical safety thresholds. The approach requires vibration data from the undamaged structure in combination with a numerical model, and is universally applicable to a wide range of structures and damage-sensitive features. Secondly, a method is proposed to analyze the minimum localizable damage. The results show that optimal localizability is a compromise between high localization resolution, high detectability, and few false localization alarms. Thirdly, a sensor placement strategy is devised that takes as input the desired minimum diagnosable damage and optimizes the sensor layout and the number of sensors accordingly. The method allows one to focus the global damage diagnosis on local structural components. Ultimately, the monitoring of prestressing forces and support displacements is incorporated into the diagnostic framework, so that they can be analyzed and distinguished from changes in material properties or cross-sectional values.Besides the performance evaluation, the framework is suitable for quality control of existing instrumentation on real structures. Therefore, self-validation strategies are implemented to verify the input parameters, to validate the theoretical assumptions, and to check its effectiveness based on non-invasive tests using extra masses. The proof of concept studies based on a laboratory steel beam and a cable-stayed bridge show promising results regarding the practical application of the theoretical contributions.; La surveillance de l’intégrité des structures (structural health monitoring, SHM) peut prolonger l'utilisation des ponts au-delà de leur durée de vie initialement prévue, accroître la sécurité entre les inspections programmées et permettre une inspection prioritaire après des événements extrêmes. L'un des principaux défis consiste à déterminer quels endommagements peuvent ou non être diagnostiqués (c'est-à-dire détectés ou localisés), ce qui est essentiel pour évaluer la valeur d'un système de SHM avant son installation et également en conséquence pour optimiser le placement des capteurs.Cette thèse développe un cadre pour prédire les endommagements détectables, c'est-à-dire le changement minimum dans des paramètres structurels locaux qui peut être détecté de manière fiable sur la base des caractéristiques calculées à partir des données vibratoires. Le diagnostic est considéré comme "fiable" si la probabilité de fausses alertes est faible et la probabilité de détection élevée. De même, un endommagement est "détectable" si le changement dans les caractéristiques est important compte tenu des incertitudes typiques liées à l'excitation ambiante, le bruit de mesure et les seuils de sécurité empiriques. L'approche requiert des données vibratoires de la structure intacte et un modèle numérique, et est universellement applicable à un large éventail de structures et de caractéristiques. Par la suite, une méthode est proposée pour analyser les endommagements localisables. Les résultats montrent que la localisation optimale est un compromis entre une haute résolution de localisation, une grande détectabilité et peu de fausses alarmes de localisation. Avec ces résultats, une stratégie de placement de capteurs est élaborée en tenant compte de la diagnosticabilité d’endommagements souhaitée et optimise le placement et le nombre de capteurs en conséquence. La méthode permet d’axer le diagnostic global d’endommagements sur les composantes structurelles locales. Enfin, le suivi des forces de précontrainte et des déplacements d’appui est intégré dans les méthodes de diagnostic, afin que cela puisse être analysé et distingué des changements de propriétés des composantes structurelles.Outre l'évaluation des performances, les méthodes développées conviennent au contrôle de la qualité de l’instrumentation existante sur des structures réelles. Par conséquent, des stratégies d'auto-validation sont mises en œuvre pour vérifier les paramètres d'entrée, valider les hypothèses théoriques et vérifier l’efficacité sur des tests non invasifs utilisant des masses supplémentaires. Les études de preuve de concept basées sur une poutre en laboratoire et un pont à haubans montrent des résultats prometteurs en ce qui concerne l'application pratique des contributions théoriques.

This paper brings up a novel method for detecting induction motor stator winding faults at an early stage. The contribution of the work comes from the delicate handling of motorvibration by applying envelope analysis, which makes it possible to capture electrical short-circuit signature in mechanical signals, even if the magnitude of the fault is fairly incipient. Conventional induction motor condition-based maintenance methods usually involve current and voltage measurements, which could be expensive to collect, and vibration-based analysis is often only capable of detecting the fault when it is already quite significant. In contrast, the solution presented in this study provides a refreshing perspective by applying time synchronous averaging to remove the discrete frequency component, and amplitude demodulation to further enhance the signal with the help of kurtogram. Experimental results on a three-phase induction motor show that the method is also able to distinguish different fault severity levels.

Low frequency vibration monitoring has significant implications on environmental safety and engineering practices. Vibration expressed by visual information should contain sufficient spatial information. RGB-D camera could record diverse spatial information of vibration in frame images. Deep learning can adaptively transform frame images into deep abstract features through nonlinear mapping, which is an effective method to improve the intelligence of vibration monitoring. In this paper, a multi-modal low frequency visual vibration monitoring system based on Kinect v2 and 3DCNN-ConvLSTM is proposed. Microsoft Kinect v2 collects RGB and depth video information of vibrating objects in unstable ambient light. The 3DCNN-ConvLSTM architecture can effectively learn the spatial-temporal characteristics of muti-frequency vibration. The short-term spatiotemporal feature of the collected vibration information is learned through 3D convolution networks and the long-term spatiotemporal feature is learned through convolutional LSTM. Multi-modal fusion of RGB and depth mode is used to further improve the monitoring accuracy to 93% in the low frequency vibration range of 0–10 Hz. The results show that the system can monitor low frequency vibration and meet the basic measurement requirements.

Electromagnetic dampers are emerging as alternatives to conventional dampers applied to stay cables of bridges because they can reduce maintenance costs and allow vibration monitoring owing to their permanent driving characteristics and self-generation function. In this study, the main equations (including those for the induced electromotive force of the active coils and the total damping force of the damper) were derived through magnetic circuit analysis using the main parameters of the electromagnetic damper model. Characteristic tests were performed on electromagnetic damper prototypes to analyze the hysteretic dynamics and derive characteristics according to their structure and excitation conditions. On the basis of the results, we proposed a regenerative hybrid electrodynamic damper with an oxygen-free copper tube and teeth structure. Its physical and electromagnetic behaviors were examined through an electromagnetic analysis of the finite element model of the proposed damper. The results confirmed that attenuation occurred via strengthened magnetic flows, and the estimated power production is suitable for energy harvesting applications. Therefore, we confirmed the feasibility of constructing a system that can simultaneously perform cable attenuation and vibration monitoring using the proposed damper.

In this paper, the problem of optimal sensor arrangement during vibration monitoring is analysed. The wave propagation caused by horizontal excitation is investigated to predict the areas of the largest ground and structure response. The equations of motion for a transversally isotropic elastic medium with appropriate absorbing boundary conditions are solved using the finite element method (FlexPDE software). The possibility of an amplified soil medium response is examined for points located on the ground surface and at various depths. The results are presented in the form of a dimensionless vibration reduction factor, defined as the ratio of the peak particle velocity observed at the selected depth to the corresponding value observed at the ground surface. Significant amplifications (&asymp, 50%) can be observed below the ground surface, especially in the case of a weak layer below a stiff layer. The effect of vibration amplification is most significant near the boundary surface of two layers. For the points located on the ground surface, the greatest peak particle velocities are observed in the direction perpendicular to the load direction. However, the greatest vertical velocity component at the ground surface is observed in front of the applied force.

Estimating the remaining useful life (RUL) of components is a crucial task to enhance reliability, safety, productivity, and to reduce maintenance cost. In general, predicting the RUL of a component includes constructing a health indicator () to infer the current condition of the component, and modelling the degradation process in order to estimate the future behavior. Although many signal processing and data-driven methods have been proposed to construct the , most of the existing methods are based on manual feature extraction techniques and require the prior knowledge of experts, or rely on a large amount of failure data. Therefore, in this study, a new data-driven method based on the convolutional autoencoder (CAE) is presented to construct the . For this purpose, the continuous wavelet transform (CWT) technique was used to convert the raw acquired vibrational signals into a two-dimensional image, then, the CAE model was trained by the healthy operation dataset. Finally, the Mahalanobis distance (MD) between the healthy and failure stages was measured as the . The proposed method was tested on a benchmark bearing dataset and compared with several other traditional construction models. Experimental results indicate that the constructed exhibited a monotonically increasing degradation trend and had good performance in terms of detecting incipient faults.

Vibration Monitoring is a particular kind of Condition Monitoring meant to infer the state of health of a machine from accelerometric measurements. From a practical point of view, the scope is then to extract from the acceleration data some valuable diagnostic information which could be used to detect the presence of possible damages (i.e., to produce knowledge about the state of health). When the monitoring is implemented online, in a continuous way, the raw accelerometric data sets can be very large and complex to be dealt with, as usually involve multiple channels (i.e., multiple locations and directions) and high sample rates (i.e., order of ksps - 103 samples per second), but the final knowledge about the state of health can, in principle, be summarized by a single binary information (i.e., healthy - 0 vs damaged - 1). This is commonly called Damage Detection. In this work, the big data management challenge is tackled from the point of view of statistical signal processing, so as to aggregate the multivariate data and condense them into single information of distance with respect to a healthy reference condition (i.e., the Novelty). When confounding influences (such as the work condition or the environmental condition) can be disregarded, the novelty information has a direct correspondence to the health information, so that an alarm indicating the detection of damage can be triggered upon exceeding a selected threshold for the limit novelty. Many different ways of solving such a binary classification problem can be found in the literature. Starting from the simplest, some of the more effective are compared in the present analysis, to finally select a reliable procedure for the big data management in vibration monitoring.

会議名/ Conference Name : APSSRA2020, 回次 / Conference Sequence : 7, 開催期間 / Conference Date : October 4-7, 2020, 開催会場 / Conference Venue : The University of Tokyo, 開催地 / Conference Place : Tokyo, 開催国 / Conference Country : JPN

It is rather unusual that the vibrations induced by the traffic, even if heavy, cause damage on the structures, whereas they are a common reason of environmental nuisance affecting residents. On the other hand, the effect of the traffic vibrations on the finishes is usually disregarded as not affecting the structural safety of the building. Nevertheless, in the context of the historical buildings such an effect may become relevant. This paper summarises the preliminary results of the effects of the traffic vibrations monitored on buildings, with special attention to the historical and artistic surface coatings. Two historical Palaces are investigated. A special procedure is developed in order to deal with the monitoring data and to extract the relevant features. After a short introduction describing the state-of-art, Sect. 2 reports the equipment used to acquire the vibration data and describes the monitored historical building, and Sect. 3 details the procedures developed to capture and analyse the meaningful events. The paper is closed with some conclusions.

Выполнен анализ существующих методов определения собственных частот крутильных колебаний валопроводов энергетических турбоагрегатов, рассмотрены возможности использования изменений собственных частот для диагностики повреждений валов. Проведённое расчётное моделирование крутильных колебаний валопровода с кольцевой трещиной позволило определить информативные собственные частоты для выявления повреждений, установить необходимую точность и места измерений собственных частот. Сделана оценка изменений собственных частот от глубины и характерных мест расположения кольцевой трещины. Рассмотрен альтернативный метод выявления нарушений целостности валопровода, основанный на контроле изменения углов статического закручивания., The methods of solving the natural frequencies of torsional vibrations of power turbines shaft is done. Determination of shaft damage by changing the natural frequencies of torsional vibrations. A model of a shaft line with a circumferential crack has been created. The calculated torsional vibrations of the shaft line model with a circumferential crack are determined. A technique has been created for determining damage to the shaft line and the necessary measurement accuracy. Calculation of changes in the natural frequencies of torsional vibrations from the location of a circumferential crack. Shafting diagnostics for monitoring changes in the angles of static twisting., Электрические станции, Выпуск 6 (1067) 2020

A new methodology for digital image processing, namely the Motion Magnification (MM), allows to magnify small displacements of large structures. MM acts like a microscope for motion in video sequences, but affecting only some groups of pixels. The processed videos unveil motions hardly visible with the naked eye and allow a more effective frequency domain analysis. We applied the MM method to several historic structures, including a 1:10-scale mockup of Hagia Irene in Constantinople tested on shaking table, the so-called Temple of Minerva Medica in Rome and the Ponte delle Torri of Spoleto. MM algorithms parameters were calibrated by comparison with reference consolidated modal identification methods applied to conventional velocimeters data. Encouraging results were obtained in terms of vibration monitoring and modal analysis for dynamic identification of the studied structures, offering a low-cost, viable support to the standard vibration sensing equipment, such as contact velocimeters, laser vibrometers and others.

The blasting for bedrock excavation on land for open channel project has a great influence on lock gate in Taishan Nuclear Power Station, therefore, based on blasting vibration monitoring data, the attenuation law of blasting vibration signal has been studied through regression analysis of practical test data by Sadaovsk empirical formula and corresponding time-frequency characteristics was analyzed by Empirical Mode Decomposition based on Hilbert-Huang transform. As for those monitoring data, the results of blast vibration velocity for vertical direction are generally larger than horizontal radial and horizontal tangential direction in the near field of blasting source and the peak particle velocity of vertical direction is usually lower than horizontal radial and horizontal tangential direction in the far field of blasting source; at the same time, their main vibration frequency mostly vary from 10 Hz to 80 Hz which is much higher than natural frequency of lock gate and is beneficial to structural safety and stability of surrounding rock mass for reducing the probability of resonance. To ensure the safety of lock gate, it is of great significance to control maximum explosive weight per delay in advance for different distance from monitoring point to the explosion source according to Safety Regulations for Blasting (GB6722-2014), which shows the excellent effect on blasting damage control of the lock gate and surrounding rock mass. The results from the analysis can be for reference to similar blasting design and blasting construction.

Purpose The purpose of this research paper is to develop and analyze micro-electro-mechanical systems sensor for vibration monitoring of pumping aggregate. Design/methodology/approach The system is based on smart sensor and smart mobile phone. Findings The numerous measurements on a wide range of turbo aggregates were performed to establish the operating condition of pumping aggregates. Originality/value Afterwards, the influence of vibration at different positions on the output vibration of the pumping aggregate was analyzed by adaptive neuro fuzzy inference system method.

A turbomachine is a fundamental engineering apparatus meant to transfer energy between a rotor and a fluid. Turbomachines are the core of power generation in many engineering applications such as electric power generation plants, aerospace, marine power, automotive etc. Their relevance makes them both mission critical and safety critical in many fields. To foster reliability and safety, then, continuous monitoring of the rotor is more than desirable. One promising monitoring technique is, with no doubt, the Blade Tip-Timing, which, being simple and non-invasive, can be easily implemented on many different rotors. Blade Tip-Timing is based on the recording of the time of arrival of the blades passing in front of a probe located at a fixed angular position. The non-contact nature of the measurement prevents influences on the measured vibration, that can be recovered for all the blades simultaneously, possibly even online. In this regard, a novel algorithm is presented in this paper for obtaining a good estimate of the vibration of the blades with minimum system complexity (i.e., only one Blade Tip-Timing probe) and minimum computational effort, so to create a simple vibration monitoring system, potentially implementable online. The methodology was tested on a dataset from a SAFRAN turbomachine made available during the Surveillance 9 international conference for a diagnostic contest.

This paper develops a novel hybrid feature learner and classifier for vibration-based fault detection and isolation (FDI) of industrial apartments. The trained model extracts high-level discriminative features from vibration signals and predicts equipment state. Against the limitations of traditional machine learning (ML)-based classifiers, the convolutional neural network (CNN) and deep neural network (DNN) are not only superior for real-time applications, but they also come with other benefits including ease-of-use, automated feature learning, and higher predictive accuracies. This study proposes a hybrid DNN and one-dimensional CNN diagnostics model (D-dCNN) which automatically extracts high-level discriminative features from vibration signals for FDI. Via Softmax averaging at the output layer, the model mitigates the limitations of the standalone classifiers. A diagnostic case study demonstrates the efficiency of the model with a significant accuracy of 92% (F1 score) and extensive comparative empirical validations.

The constant development of geotechnical technologies imposes the necessity of monitoring techniques to provide a proper quality and the safe execution of geotechnical works. Several monitoring methods enable the preliminary design of work process and current control of hydrotechnical works (pile driving, sheet piling, ground improvement methods). Wave parameter measurements and/or continuous histogram recording of shocks and vibrations and its dynamic impact on engineering structures in the close vicinity of the building site enable the modification of the technology parameters, such as vibrator frequency or hammer drop height. Many examples of practical applications have already been published and provide a basis for the formulation of guidelines, for work on the following sites. In the current work the author’s experience gained during sheet piling works for the reconstruction of City Channel in Wrocław (Poland) was presented. The examples chosen describe ways of proceedings in the case of new and old residential buildings where the concrete or masonry walls were exposed to vibrations and in the case of the hydrotechnical structures (sluices, bridges).

Fiber grating is a kind of new type of fiber optic light source device which has been rapidly changing in the refractive index of the core in recent years. Especially, it can realize the high precision of the external parameters by means of the special structure design and the encapsulation technology [1, 2]. In this paper, a fiber grating vibration sensor which is suitable for vibration monitoring in key areas is designed based on the technical background of vibration monitoring system. The sensor uses a single beam structure and pastes the fiber Bragg grating (FBG) to measure the vibration wavelength on the surface. When the vibration is simply harmonic vibration, the Bragg reflection wavelength will change periodically, and the periodic variation of the wavelength curve can be measured by the fiber grating demodulator, then the correctness of the experimental results is verified. In this paper, through the analysis of the data measured by the demodulator, the MATLAB software is used to verify the data, and the different frequency domains, the modes, and the phase frequency curves are obtained. The measurement range is 0 Hz–100 Hz, and the natural frequency is 90.6 Hz.

Vibration condition monitoring (VCM) enhances the performance of Hydro Generating Equipment (HGE) by minimizing the damage and break down chances, so that equipment stay available for a longer time. The execution of VCM and diagnosing the system of an HPS includes theoretical and experimental exploitation. Various studies have made their contribution to find out the vibration failure mechanism and incipient failures in HPS. This paper gives a review on VCM of electrical and mechanical equipment used in the HPS along with a brief explanation of vibration related faults considering past literature of around 30 years. Causes of the vibrations on rotating and non-rotating equipment of HPS have been discussed along with the standards for vibration measurements. Future prospectus of VCM is also discussed.