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During the gear hobbing machine process, the hob performance degradation assessment is significant for optimizing the tool changing frequency and improving machining efficiency. It is challenging to recognize hob wear state through vibration analysis and signal processing. Especially in the condition of intense vibration and noise interference, extracting signal features that reflect the wear state is challenging work. This paper proposes a feature extraction method called Cyclic Statistical Energy (CSE) to obtain the hob wear characteristics by tracking the sensitive frequency band. For the method, the vibration signal model of hob spindle is established first based on the vibration-generation mechanism of the machining process. Then, an index E is proposed to track the wear resonance frequency band of the vibration signal. Furthermore, the cyclic statistical order is analyzed and discussed. The hob performance degradation assessment can be realized by analyzing the energy index E with time variation. The experimental setup has been designed, and the two tests have been conducted in the production line: (1) impact hammer test and (2) hob whole life cutting test. The impact hammer test is set to obtain the modal parameters of the hob. Based on this, the whole life cutting test is designed and realized by which the vibration data of whole life with different wear states can be acquired. The proposed method CSE is successfully applied into online industry experiments test. The results show that the proposed method can give a more accurate performance degradation assessment curve for tool condition monitoring compared with traditional methods such as Wavelet Packet Decomposition (WPD). [ABSTRACT FROM AUTHOR]

Rolling element bearings are at the heart of most rotating machines and they bear the function of connectivity between the rotor and stator. It is important to distinguish the incipient fault before the bearing step into serious failure. The Slip Matrix (SM) construction method based on Singular Value Decomposition (SVD) is proposed in this paper. The SM based fault feature extraction and impulses intelligent detection methods are introduced as the key steps for rolling bearing fault diagnosis. The numerical simulation of rolling bearing fault signal is adopted which shows that the proposed method is good at fault impulses detection in strong background noise environment. The rolling element bearing accelerated life test is performed for the acquisition of experimental data of rolling bearing fault. With the rolling bearing running from normal state to failure, the initial fault signal part can be picked out from the whole life vibration data of the rolling bearing. The vibration signal is close to the nature fault signal which is acquired from a rolling bearing applied in industrial field. The analysis result shows that the proposed method has an excellent performance in rolling bearing fault detection. [ABSTRACT FROM AUTHOR]

Abstract: Rolling element bearing faults are among the main causes of breakdown in rotating machines. In this paper, a rolling bearing fault model is proposed based on the dynamic load analysis of a rotor-bearing system. The rotor impact factor is taken into consideration in the rolling bearing fault signal model. The defect load on the surface of the bearing is divided into two parts, the alternate load and the determinate load. The vibration response of the proposed fault signal model is investigated and the fault signal calculating equation is derived through dynamic and kinematic analysis. Outer race and inner race fault simulations are realized in the paper. The simulation process includes consideration of several parameters, such as the gravity of the rotor-bearing system, the imbalance of the rotor, and the location of the defect on the surface. The simulation results show that different amplitude contributions of the alternate load and determinate load will cause different envelope spectrum expressions. The rotating frequency sidebands will occur in the envelope spectrum in addition to the fault characteristic frequency. This appearance of sidebands will increase the difficulty of fault recognition in intelligent fault diagnosis. The experiments given in the paper have successfully verified the proposed signal model simulation results. The test rig design of the rotor bearing system simulated several operating conditions: (1) rotor bearing only; (2) rotor bearing with loader added; (3) rotor bearing with loader and rotor disk; and (4) bearing fault simulation without rotor influence. The results of the experiments have verified that the proposed rolling bearing signal model is important to the rolling bearing fault diagnosis of rotor-bearing systems. [Copyright &y& Elsevier]

Abstract: Rolling element bearing faults are among the main causes of rotating machines breakdown. It is important to distinguish the incipient fault before the bearings step into serious failure. Based on the traditional singular value decomposition (SVD) theory, short-time matrix series (STMS) and singular value ratio (SVR) are introduced to the vibration signal processing. The proposed signal processing method is called S-SVDR (STMS based SVD method using SVR) and it has been proved to have a good local identification capability in the rolling bearing fault diagnosis. The detailed description of applying S-SVDR methods to rolling bearing fault diagnosis is given through the artificial fault signal processing in experiment 1. In experiment 2, rolling element bearing accelerated life test is performed in Hangzhou Bearing Test & Research Center (HBRC). The experimental result shows that the incipient fault can be well detected through S-SVDR processing method. However, the envelope analysis of original signal cannot detect the fault due to the existence of signal interference. A conclusion can be made that the proposed S-SVDR method has a good effect on de-noising and eliminating the signal interference of rolling bearing for the fault diagnosis. [Copyright &y& Elsevier]

An energy analyzing method called the Impact Energy Model (IEM) is introduced to assess and diagnose the rub–impact fault of a rotor system. The main purpose of the model is to indicate the probability of rub–impact or the severity level of the fault. Utilizing the nonlinear predictive energy, an effective quantitative evaluation index IEM is obtained from the model. Some work has been done elsewhere to partly demonstrate the IEM through experiments. In this paper we mainly discuss the variation of the IEM index with the speed ratio Ω at different levels of damping ratio ξ. The stagnation point is discussed in detail which is important to the assessment for the probability or severity of a rub–impact fault. The influence of dynamic stiffness is discussed which may affect the assessment result of the IEM. Furthermore, a sensitive area of the rotor operating system is presented. The result shows that this sensitive area provides a reference for the operation and design of a rotor system. [ABSTRACT FROM PUBLISHER]

Spectral kurtosis (SK) is an algorithm that gives an indication of how kurtosis varies with frequency. A frequency band that contains abundant information, especially the impact signal, can be tracked by calculating SK. In the present article, SK combined with Autoregressive AR model, was applied into the fault diagnosis and condition monitoring of bearings. Accelerated life test of rolling bearings in Hangzhou Bearing Test & Research Center (HBRC) was performed to collect vibration data over their entire lifetime (normal-fault-failure). The result shows that SK can detect early incipient fault by eliminating some other interfering frequency components. In addition, it can detect fault 5 min earlier than root mean value (RMS). This fault detection in advance is significant for condition monitoring. [ABSTRACT FROM AUTHOR]

Abstract: An experimental setup which can simulate the rotor-to-stator rub in a rotor system is installed. A rub screw is used to simulate the condition of local rub–impact fault. Based on the theory of elastic collision and energy conservation, an Impact Energy Model (IEM) is proposed to evaluate the probability or severity of rub–impact fault. To prove this model, the paper conducts the experiment in two steps i.e. hammer test and rub–impact fault validation. The wave signal, spectrum and axis orbit are used to analyze the severity of the rub–impact fault when it occurs. The analysis result shows that the proposed Impact Energy Model (IEM) is effective in the assessment of rub–impact fault. Furthermore, the proposed IEM can also provide a reference for the design and operation of a rotor system. [Copyright &y& Elsevier]

Robotics is becoming increasingly important in modern engineering areas. After the big nuclear accidents in Fukushima 2011, the crossing research of nuclear and robotics draws more and more attentions. In this paper, a method for radioactive source recognition using moving Compton camera imaging robot is proposed. In order to assess the quality of the reconstructed image, a new method called Cross-section Outline at Half Maximum (COHM) is proposed for three dimensional image reconstruction precision assessments. Two experiment validations using Geant4 platform are given which include the measurement with different distances and angles respectively. The result shows that the application of Compton camera imaging robot can improve the radioactive source image reconstruction quality. Based on the result of the experiment, several preconditions and suggestions are given to improve the image reconstruction precision when we apply robot to the radioactive source recognition. The research is focused on the three dimensional image reconstruction by using Compton camera imaging robot. It can improve the radioactive source recognition precision especially for three-dimensional contour recognition. And also it can supply good reference for the robot routine planning when we apply the robot into radioactive source recognition in future. • Moving Compton camera imaging robot is proposed for radioactive source recognition. • COHM is proposed for three dimensional image reconstruction precision assessments. • Preconditions and suggestions are given to improve reconstruction precision. [ABSTRACT FROM AUTHOR]

Compressed Air Energy Storage (CAES) is considered as one of the key solutions to handle intermittent and random wind power. However, limited energy conversion efficiency and high capital cost of energy storage have restricted significantly the integration of wind power with CAES. In this study, a grid-connected power optimization strategy based on piecewise averaging of real-time wind power and electricity price data is developed to ensure continuous and stable power outputs to the grid using modified profit-maximizing algorithm. Thermodynamic analysis on the performance of low-temperature adiabatic CAES, energy conversion, and economic evaluation were carried out for a hybrid wind/low-temperature adiabatic CAES system (wind/LA-CAES) with pressure vessels. The proposed optimization strategy reduced the required capacity of CAES and the levelized cost of electricity (LCOE) significantly with greater utilization of wind power and operation profitability. The findings presented in this study is of significant reference value to future development of large-scale wind power integrated with CAES. [ABSTRACT FROM AUTHOR]

The failure of rolling bearings is one of the most important factors for rotating machinery breakdown. The detection of initial fault in rolling bearings is crucial for the further prevention of equipment malfunction and failure. In this study, a new rolling bearing fault diagnosis method based on the singular value decomposition, slip Hankel matrix series construction and maximum singular value energy analysis is proposed. It has been validated that the proposed method has an excellent impulse recognition capacity, which can be further applied to design the optimal band‐pass filter for rolling bearing fault diagnosis. Then, the minimum entropy deconvolution (MED) technique is introduced to improve the fault extraction ability of the proposed method. Simulated signals and artificial fault tests are used to prove the capacity of the new method for rolling bearing fault detection. Furthermore, the result of accelerated life test indicates the initial bearing fault can be recognised by the proposed method, while the envelope spectrum cannot directly distinguish the failure type because of the redundant frequency interference. It can be concluded that the proposed method has the effectiveness of initial fault identification and redundant frequency elimination for rolling bearing fault diagnosis. [ABSTRACT FROM AUTHOR]

This paper proposes a novel and powerful pattern recognition method named symmetrical singular value decomposition representation (SSVDR) and presents its application to face recognition. The SSVDR method is based on singular value decomposition (SVD) and symmetry prior. In this method, the given image is firstly decomposed into a composition of a set of base images by the singular value decomposition technique. Then, the first few base images (which can be proved to be the low-frequency asymmetrical base images) are turned into symmetrical base images according to facial symmetry. Finally, a new representation of the original image is reestablished for the final recognition. For evaluating the performance of the SSVDR method, some experiments are conducted in two famous face databases: extended Yale B and CMU-PIE database. The experiment results show the proposed SSVDR method can reestablish a new homogeneous representation of the original image and has an encouraging performance on face recognition compared with the current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• Novel CRB vibration model considering contact stiffness and natural frequency characteristics. • The outer raceway spalling fault with varying sizes is simulated in this paper, considering spalling defect extents and load distribution. • This paper showed that the effects of contact stiffness variation on natural frequency characteristics by the statistical distribution of fault feature signals. The modeling of early spalling defects in cylindrical roller bearings (CRB) is a challenging problem in fault diagnosis. Due to the absence of prominent "drop-in" excitation, accurately describing the signal characteristics of the early failure mechanism model for CRBs becomes difficult. In this paper, the failure of cylindrical roller bearings due to spalling caused by fatigue is a frequent occurrence. The previous defect models have been centered on sharp-edge impact between the roller and spalling defects. However, they have not provided an adequate depiction of the variation in contact stiffness caused by roller-fault interaction. In this paper, a novel CRB vibration model that takes into account contact stiffness and natural frequency characteristics is proposed. The model considers both the load redistribution and contact stiffness variation caused by spalling defects. This new model provides a spring-damper system based on contact stiffness between the roller and raceway, enabling an estimation of the CRB system's natural frequency characteristics. The outer raceway spalling fault with varying sizes is simulated in this paper, taking into account spalling defect extents and load distribution. The vibration response of the fault signal model is investigated. The simulation results show that the load and contact stiffness will change as the roller passes through the defect zone. Furthermore, the statistical distribution of fault feature signals demonstrates the effects of contact stiffness variation on natural frequency characteristics. The experiments given in this paper have verified the simulation results of the proposed signal model. [ABSTRACT FROM AUTHOR]

• A method for constructing grayscale images of vibration signals has been proposed. • An unconventional convolutional kernel design method has been proposed. • The validity of the fault diagnosis method in this paper were verified. Rolling bearings are widely used in various industries, including rail transit, aerospace, and wind power generation, playing a critical role. However, bearing failures can lead to serious consequences, impacting equipment operation and even causing safety accidents. Hence, the diagnosis of bearing faults is of utmost importance. However, the variable speed conditions experienced during bearing operation pose significant challenges to fault diagnosis. To overcome the limitations of traditional methods in diagnosing bearing faults under variable speed conditions, this paper proposes a fault diagnosis method based on the gray-level co-occurrence matrix (GLCM) and Dual Channel Convolutional Neural Network (DCCNN). The method introduces a two-dimensional grayscale matrix construction (2D-GMC) technique to extract grayscale texture features for fault diagnosis. Additionally, an unconventional kernel design method, based on grayscale image contrast, is proposed to reduce the complexity associated with traditional square kernels. A new DCCNN architecture is developed accordingly. Furthermore, the transfer learning concept is utilized to train the proposed DCCNN model using fault signals at specific rotational speeds. The method intercepts the variable speed into multi-speed short-time series, then constructs gray image under different speed to realize the rapid fault diagnosis of bearings under variable speed conditions. [ABSTRACT FROM AUTHOR]

The integrated electricity and heating system (IEHS) can improve energy efficiency and promote renewable energy consumption. When quality-quantity regulation (QQR) is adopted, namely adjusting hydraulic and thermal conditions of heating system, the increase in operation flexibility is more remarkable. However, present IEHS dispatch models considering QQR overlook hydraulic characteristics of valves and variable frequency pumps resulting in inexecutable dispatch results easily and hindering IEHS's flexibility potential. Furthermore, loop networks and the access of multiple heat sources in heating systems may lead to flow reversal, but current research adopts a fixed flow direction which suppresses the potential of flow reversal to improve flexibility. In this paper, we propose an IEHS dispatch model considering QQR to promote renewable energy consumption, which considers hydraulic characteristics of valves and variable frequency pumps, and flow reversal. Specifically, we introduce two binary flow direction labels for every branch in heating system model and then the IEHS dispatch model which can deal with flow reversal is established. A sequential solution process combining linear and nonlinear optimization is formulated to overcome the non-convex feature of IEHS dispatch model. Specifically, piecewise linearization and piecewise McCormick relaxation are combined to handle complex nonlinear terms in the dispatch model. Therefore, a mixed integer linear programming model is obtained and solved, of which results are used as initial values for nonlinear optimization. Results in the case study show that operation cost is decreased by 0.97 % and renewable power consumption rate is increased from 83.31 % to 84.42 % after considering valve adjustment. Operation cost is further decreased by 6.06 % and renewable power consumption rate is increased to 94.04 % after considering flow reversal. • IEHS dispatch model with quality-quantity regulation of heating systems is proposed. • Operation characteristics of pumps and valves in heating system is considered. • Heating system model with flow reversal is constructed. • A sequential method combining linear and nonlinear optimization is formulated. • Impact of valve adjustment and flow reversal on IEHS dispatch is investigated. [ABSTRACT FROM AUTHOR]

Due to planetary movement of planet gears, the vibration signal perceived by a stationary sensor is modulated and difficult to diagnose. This paper proposed a vibration separation methodology compensated by a time-varying transfer function (TVTF-VS), which is a further development of the vibration separation (VS) method in the diagnosis of non-hunting tooth planetary gearboxes. On the basis of VS, multi-teeth VS is proposed to extract and synthesize the meshing signal of a planet gear using a single transducer. Considering the movement regularity of a planetary gearbox, the time-varying transfer function (TVTF) is represented by a generalized expression. The TVTF is constructed using a segment of healthy signal and an evaluation indicator is established to optimize the parameters of the TVTF. The constructed TVTF is applied to overcome the amplitude modulation effect and highlight fault characteristics. After that, experiments with baseline, pitting, and compound localized faults planet gears were conducted on a non-hunting tooth planetary gearbox test rig, respectively. The results demonstrate that incipient failure on a planet gear can be detected effectively, and relative location of the local faults can be determined accurately. [ABSTRACT FROM AUTHOR]

Vibration signals are widely used for bearing fault detection and diagnosis. When signals are acquired in the field, usually, the faulty periodic signal is weak and is concealed by noise. Various de-noising methods have been developed to extract the target signal from the raw signal. Stochastic resonance (SR) is a technique that changed the traditional denoising process, in which the weak periodic fault signal can be identified by adding an expression, the potential, to the raw signal and solving a differential equation problem. However, current SR methods have some deficiencies such us limited filtering performance, low frequency input signal and sequential search for optimum parameters. Consequently, in this study, we explore the application of SR based on the FitzHug-Nagumo (FHN) potential in rolling bearing vibration signals. Besides, we improve the search of the SR optimum parameters by the use of particle swarm optimization (PSO). The effectiveness of the proposed method is verifie d by using both simulated and real bearing data sets. [ABSTRACT FROM AUTHOR]

As an essential part of the transmission system, gearboxes are considered as a major source of vibration. Signal identification of gear vibration is necessary for online monitoring of the mechanical systems. However, in engine-gearbox systems, the ignition impact of the engine is strong, so that the gear vibration is generally submerged. To overcome this issue, the resonance-based signal sparse decomposition (RSSD) method is used in this paper based on different oscillatory behaviors of the gear meshing impact and the engine ignition impact. To improve the accuracy of RSSD under interferences, the meshing frequency energy ratio (MF–ER) index is introduced into RSSD to adaptively choose the decomposition parameters. Before applying the RSSD method, the auto-regression (AR) model is used as a pre-whitening step to eliminate the normal gear meshing vibration, which improves the decomposition performance of RSSD. The effectiveness of the proposed AR-ORSSD (AR-based optimized RSSD) algorithm is tested using both simulated signals and measured vibration signals from an engine-gearbox system in a forklift. Comparisons were made with the RSSD algorithm based on a genetic algorithm. Experimental results indicate that the AR-ORSSD algorithm is superior at identifying gear vibration signals especially when under strong interferences. [ABSTRACT FROM AUTHOR]

Gearboxes are at the heart of most rotating machines and they are considered as one of the main sources of vibration. As a key element in rotating machines, it is important to extract the gearbox vibration part from the mechanical system signal to assess the health state of the gearbox. In general, the gear meshing frequency part contains rich information which reflects its health state. In this paper, the authors find that in some cases when gearboxes work under heavy load, the meshing frequency part cannot be detected easily from the frequency spectrum. We think that the meshing frequency part may be modulated to the higher frequency band as the meshing impacts. To prove this point, a Multi-Input Single-Output (MISO) model is proposed to identify the local resonance excited by gear meshing impacts. In our method, a Meshing Impact Energy Distribution (MIED) graph is obtained through iteration to determine the demodulated frequency band. Experimental vibration data acquired form a healthy gearbox are illustrated to validate the performance of the proposed method. The forklift is taken as an example in the paper. Forklifts usually work in heavy load condition, and the gearbox vibration of a forklift is generally mixed with the ignition impacts of the diesel engine. The experiment result shows that for forklift machine system working in heavy load condition, its meshing frequency part of gearbox vibration signal is modulated into high frequency band. Further, the result shows that the proposed method is good at extracting the meshing frequency component when it is modulated into high frequency band especially when it is in heavy load condition. [ABSTRACT FROM AUTHOR]