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With the development of concepts of industry 4.0, condition monitoring techniques are changing. Large amounts of generated data require diagnostic procedures to be automated, which drives the need for new and better methods of autonomous interpretations of vibration condition monitoring data. However, if new methods are to be operational, they need to be verified under real industrial conditions and compared with well-established expert-based diagnostic techniques. This article introduces the novel algorithm of data preprocessing for the nearest-neighbor-based anomaly detection. This approach is validated on real industrial machinery in a series of case studies. The population of over-hung centrifugal fans, employed in the same industrial process, were monitored continuously according to the proposed methodology for an extended time period. Piezoceramic accelerometers were used to register time-domain vibration data. The data were processed to extract several signal features to serve as inputs to the anomaly detection algorithm. The novel solution is compared to the well-established condition monitoring approach. Presented data include not only the intact state of machinery but also a machine breakdown case and serious deterioration of the machine condition. The influence of maintenance work is also presented in the article. Authors show the data-driven approach to condition monitoring, which can be used as one of many predictive maintenance techniques.

This paper presents the use of laser vibrometer measurements to detect and locate damage in a metal plate. An algorithm based on local spatial filters was selected, and for the purpose of comparison, the fault location was also determined based on the wavelet analysis of mode shapes. The research was carried out first on the created finite element model of aluminum plate, where two kinds of damage of increasing size and temperature change were simulated. After obtaining positive results, a laboratory experiment was carried out, which consisted of measuring the vibration of the aluminum plate with the laser vibrometer in undamaged condition, at increased temperatures, and with various damage scenarios. The conclusions of the laboratory experiment confirm the damage detection capabilities of the methods but question their damage localization potential.

The paper presents work related to nonlinear system parameters identification. The research is focused on systems with hysteretic stiffness characteristics. The identification procedure is developed with use of artificial neural networks. The presented method assumes two separate clusters of neural networks, which are supported by additional signal processing block. Such approach gives an advantage over the conventional identification methods due to its small restrictions. The validation process considers structural responses in time and frequency domains as well as the restoring force plane of the dynamic structure. First, verification of the identification method is performed on the numerical simulation of the system with hysteretic stiffness. Next, the identification of the real dynamic system with contact-related nonlinearity is carried out. The steel samples with contacting surfaces were used in the experiment. Electromagnetic shaker was used to excite the structure and enforce a relative shear motion between surfaces in contact. The system response was recorded using the Polytec laser vibrometer.

The paper introduces the concept of Instantaneous Circular Pitch Cycle Map (ICPCM) as a tool for analysis and diagnosis of modulation signatures in vibrations generated by Epicyclic Gearbox (EGs). The method is based on the representation of a vibration signal as a 3-D map illustrating selected signal properties. Within the method, a novel concept of a Resampling Relative Order (RRO) is introduced. The paper illustrates how the ICPCM might serve as an auxiliary tool for proper interpretation of spectra, envelope spectra, and time-synchronous averaged representations. The idea of the method is presented on a simulated signal, while its practical performance is illustrated on a real signal from a multi-stage EG.

Condition monitoring is an indispensable element related to the operation of rotating machinery. In this article, the monitoring system for the parallel gearbox was proposed. The novelty detection approach is used to develop the condition assessment support system, which requires data collection for a healthy structure. The measured signals were processed to extract quantitative indicators sensitive to the type of damage occurring in this type of structure. The indicator’s values were used for the development of four different novelty detection algorithms. Presented novelty detection models operate on three principles: feature space distance, probability distribution, and input reconstruction. One of the distance-based models is adaptive, adjusting to new data flowing in the form of a stream. The authors test the developed algorithms on experimental and simulation data with a similar distribution, using the training set consisting mainly of samples generated by the simulator. Presented in the article results demonstrate the effectiveness of the trained models on both data sets.

Many engineering structures operate under non-stationary conditions resulting in signals containing time-varying frequency components. Analysis of such signals in time or frequency domain naturally yields smeared results. Typically implemented solution to this problem is the angle and order domain analysis. However, in some scenarios, acquisition of speed signal may be not available, due to practical limitations. If the speed fluctuations are limited, or recorded vibrational signal is free from environmental and measurement disturbances, it is possible to extract the speed signal from raw time waveforms. State-of-the-art methods include e.g. semi-automatic tracking of the ridges of the Short-Time Fourier Transform for speed signal reconstruction. However, these methods can fail if the time-varying frequency components cross with each other. Moreover, it is very common that complicated structures operating in non-stationary conditions are controlled by sophisticated electronic systems, which can introduce constant frequency noise to the measuring equipment. This can lead to some interference of these time-invariant and time-variant frequency components. The paper presents a simple and easy way to use a tool for hand selection and tracking of desired components, thus making complex time-frequency filtration easy. Finally, instantaneous frequency is calculated with use of Centre Moments of Frequency to extract the speed signal. The presented method shows how a particular tool developed within 3-dimensional image processing branch could be implemented in analysis of 2-dimensional signals characterized by time-frequency varying components.

Active vibration suppression is a well explored area when it comes to simple problems, however as the problem complexity grows to a time variant system, the amount of researched solutions drops by a large margin, which is further increased with the added requirement of very limited knowledge about the controlled system. These conditions make the problem significantly more complicated, often rendering classic approaches suboptimal or unusable, requiring a more intelligent approach - such as utilizing soft computing. This work proposes a Artificial Neural Network (ANN) Model Predictive Control (MPC) scheme, inspired by horizon techniques which are used for MPC. The proposed approach aims to solve the problem of active vibration control of an unknown and largely unobservable time variant system, while attempting to keep the controller fast by introducing several methods of reducing the amount of calculations inside the control loop - which with proper tuning have no negative impact on the controller’s performance. The proposed approach outperforms the multi-input Proportional-Derivative (PD) controller preoptimized using a genetic algorithm.

One of the important issues related to the applications of Lamb waves for structural health monitoring is their undesired sensitivity to variation of environmental conditions. Temperature is the main factor that can affect wave propagation and hence significantly reduce performance of a SHM system. Therefore, there is a need for development of robust monitoring methods with low sensitivity to temperature variations. This paper is aimed at verification of efficiency of four methods designed for damage detection using Lamb wave measurements performed in variable environmental conditions. The methods investigated in the comparison are the following: optimal baseline selection approach, the damage index based on a signal alignment with respect to instantaneous phase, and a group measurement approach capable of distinguishing local damage-related changes from temperature-induced global ones. The fourth method relies on fusion all these solutions simultaneously. The methods’ ability to damage detection is compared using a specimen that is subjected to large temperature changes. It is found that although all the methods have their strengths and weaknesses, a cooperation of all solutions allows for significant increase of the damage detection efficiency.

Neural networks are commonly recognized tools for the classification of multidimensional data obtained in structural health monitoring (SHM) systems. Their configuration for a given scenario is, however, a challenging task, which limits the possibilities of their practical applications. In this article the authors propose using the neural network ensemble approach for the classification of SHM data generated by guided wave sensor networks. The overproduce and choose strategy is used for designing ensembles containing different types and sizes of neural networks. The proposed method allows for a significant increase of the state assessment reliability, which is illustrated by the results obtained from the practical industrial case of a full-scale aircraft test. The method is verified in the process of detecting fatigue cracks propagating in the aircraft load-carrying structure. The long-term experiments are performed in variable environmental conditions with a net of structure-embedded piezoelectric sensors.

In the paper, optical-based measurement methods for calculating the deflection and vibration of overhead lines are presented. The authors describe the state of the art in the field of non-contact examination of static and dynamic overhead transmission line characteristics, and propose concepts of vision-based measurement systems for both static and dynamic states of a structure. The system devoted to static measurements is based on a digital SLR camera and image-processing software used for the acquisition and interpretation of data. The digital image correlation method, implemented in Wiz2D software, is applied to compute the displacement of the transmission line cable with respect to a known baseline position. Dynamic characteristics of the structure are obtained using a stereo-vision system consisting of two high-speed digital cameras. Corresponding points in two video sequences of the vibrating cable are tracked using TEMA software. 3D positions in a camera frame of reference and displacements are computed using a 3D reconstruction method. The paper describes two series of experiments conducted on a lab setup and the obtained results are examined and discussed.

The paper presents up-to-date multidisciplinary review of scientific knowledge and industrial guidelines concerning the issue of technical assessment of epicyclic gearboxes on the basis of vibrational measurement. The paper covers norms, patents, signal processing methods, technical features, diagnostic methodologies, and high-level soft computing. The review classifies recognized papers according to different aspects, like case difficulty, applied methods or studied objects. Moreover, the paper discusses recent development of diagnostic techniques taking into account changing operational conditions and requiring powerful data processing units not available hitherto. Since in recent years many partial review papers have been published in different journals, the current paper presents their individual scope. Because diagnostics of epicyclic gearboxes generally covers and extends the classical signal processing techniques and classical machine condition monitoring functionality, the current paper might serve as a descriptive database of sources of information and bibliography or as a reference to individual techniques.

Summary Deflection curve can be used to detect and localize damage in civil engineering structures. In this paper, a vision-based method applied for in-plane displacement field measurement of cantilever beams is presented. The deflection curve of the analyzed structure is computed by means of the digital image correlation. Damage is introduced into the structure. Resulting deflection curves are used as an input to the novel damage detection algorithms: line segments method and voting method. The algorithms are then compared with the second derivative method and assessed through the probability of detection procedure. Copyright © 2015 John Wiley & Sons, Ltd.

A large number of commercial systems for condition monitoring of most common planetary gearboxes used in wind turbines and mining machinery have been developed for years. However nowadays, multistage constructions are encountered in industries. These are not necessarily planetary, but generally epicyclic. Current state of the art, according to the authors knowledge, does not give general equations for a case where multistage systems are considered, where some of the gears consist all moving parts. Hence, currently available CMS systems are not suitable for condition monitoring of these kinds of systems. The paper presents a new general equation, which allows calculating the characteristic frequencies of any kind of multistage gear sets, as a result of theoretical investigation. Illustrated solution does not assume a fixed speed of any element. Moreover, presented equation takes into account corrected teeth, making developed equations most general from all available in tribology science. Presented scientific development is currently implemented in a modern European CMS.

The paper illustrates a general equation in a new form, which allows calculating the characteristic frequencies of any kind of epicyclic gear sets with a ring, a sun, and planets. Moreover, presented equation takes into account corrected teeth (i.e. where the equality 2P+S=R is not fulfilled). This happens when gearboxes contain gears where corrected teeth procedure was adopted during designing stage. Presented solution can refine the configuration modules of the Condition Monitoring Systems (CMS) in such a way that allows to configure systems into larger groups than now available, i.e. multistage gear sets systems with epicyclic gears. Such CMS are capable of early mechanical faults detection, which prevents from costly critical repairs. For instance, fault detection of wind turbines is typically based on vibration and process signals analysis. Illustrated possible enhancement of configuration module is the basis for determining the energy bands in the spectra and envelope spectra in the process of identifying characteristic frequencies caused by gear defects.