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Transient impulses caused by local faults are critical informative indicators for rolling element bearing fault diagnosis. The methods for accurately extracting transient impulses while suppressing strong background noise and interference components have received extensive studies. In this article, a novel fault diagnosis scheme based on optimized wavelet packet denoising and modulation signal bispectrum is proposed, which takes advantage of the transient impulse enhancement of wavelet packet denoising and the demodulation ability of modulation signal bispectrum to diagnose bearing faults more accurately. First, the measured signals are decomposed into a series of time–frequency subspaces using wavelet packet transform. An optimal threshold value is selected based on the proposed threshold criterion by considering unbiased autocorrelation of envelope and Gini index of the transient impulses. Subsequently, the subspaces are denoised by the wavelet packet denoising with the optimized threshold value, and the master subspaces that containing the fault-related transient impulses are selected based on the Gini index indicator. Finally, the modulation signal bispectrum is utilized to further purify the signal and extract the modulation components contained in the transient impulses, and the suboptimal modulation signal bispectrum slices are selected based on the characteristic frequency intensity coefficient. The modulation signal bispectrum detector is then obtained by averaging the suboptimal modulation signal bispectrum slices to determine the type of the bearing faults. The proposed wavelet packet denoising-modulation signal bispectrum is validated based on the simulation and experimental studies. Compared with the variational mode decomposition and Teager energy operator, fast kurtogram as well as conventional modulation signal bispectrum, the proposed wavelet packet denoising-modulation signal bispectrum method has superior performance in extracting the fault feature of the incipient defects on different bearing components.
Junchao Guo; Zhanqun Shi; Dong Zhen; Zhaozong Meng; Fengshou Gu; Andrew D Ball. Modulation signal bispectrum with optimized wavelet packet denoising for rolling bearing fault diagnosis. Structural Health Monitoring 2021, 1 .
AMA StyleJunchao Guo, Zhanqun Shi, Dong Zhen, Zhaozong Meng, Fengshou Gu, Andrew D Ball. Modulation signal bispectrum with optimized wavelet packet denoising for rolling bearing fault diagnosis. Structural Health Monitoring. 2021; ():1.
Chicago/Turabian StyleJunchao Guo; Zhanqun Shi; Dong Zhen; Zhaozong Meng; Fengshou Gu; Andrew D Ball. 2021. "Modulation signal bispectrum with optimized wavelet packet denoising for rolling bearing fault diagnosis." Structural Health Monitoring , no. : 1.
Rolling bearings are important parts of mechanical equipment. However, the early failures of the bearing are usually masked by heavy noise. This brings about difficulties to the extraction of its fault features. Therefore, there is a need to develop a reliable method for early fault detection of the bearing. Considering this issue, a novel fault diagnosis method using the improved wavelet threshold denoising and fast spectral correlation (Fast-SC) is proposed. First, to solve the discontinuity of the hard threshold function and avoid the constant deviation triggered by the soft threshold function, a piecewise continuous threshold function is proposed by using a new threshold selection rule to denoise the original signal. In the new threshold function, the adjuster α is introduced to improve the traditional wavelet denoising algorithm, so as to enhance the signal-to-noise ratio (SNR) of the original signal more effectively. Then, the denoised signal is analysed by Fast-SC to identify the rolling bearing fault features. Finally, simulation analysis and experimental data demonstrate that the proposed approach is effective for rolling bearing fault detection compared with Fast-SC and the combined method based on traditional wavelet threshold and Fast-SC.
Shaoning Tian; Dong Zhen; Junchao Guo; Haiyang Li; Hao Zhang; Fengshou Gu. Fault Diagnosis of Rolling Bearing Using Improved Wavelet Threshold Denoising and Fast Spectral Correlation Analysis. Shock and Vibration 2021, 2021, 1 -10.
AMA StyleShaoning Tian, Dong Zhen, Junchao Guo, Haiyang Li, Hao Zhang, Fengshou Gu. Fault Diagnosis of Rolling Bearing Using Improved Wavelet Threshold Denoising and Fast Spectral Correlation Analysis. Shock and Vibration. 2021; 2021 ():1-10.
Chicago/Turabian StyleShaoning Tian; Dong Zhen; Junchao Guo; Haiyang Li; Hao Zhang; Fengshou Gu. 2021. "Fault Diagnosis of Rolling Bearing Using Improved Wavelet Threshold Denoising and Fast Spectral Correlation Analysis." Shock and Vibration 2021, no. : 1-10.
Transient impulses caused by local defects are critical for the fault detection of rotating machines. However, they are extremely weak and overwhelmed in the strong noise and harmonic components, making the transient features are very difficult to be extracted. This paper proposes an adaptive multi-scale improved differential filter (AMIDIF) to enhance the identification of transient impulses for rotating machine fault diagnosis. In this scheme, firstly, the AMIDIF is performed to decompose the measured signal of rotating machine into a series of multi-scale improved differential filter (MIDIF) filtered signals. Subsequently, in view of the MIDIF filtered signals exhibit varying extents of validity in revealing fault features, a weighted reconstruction method using correlation analysis is proposed in which the weighted coefficients are counted and distributed to the corresponding MIDIF filtered signals to highlight the effective MIDIF filtered signals and weaken the invalid ones. Finally, the transient impulse components of rotating machinery are obtained by multiplying the weighted coefficients and the MIDIF filtered signals under different scales. Furthermore, the fault types of rotating machines are inferred from the fault defect frequencies in the envelope spectrum of the transient impulses. Simulation analysis and experimental studies are implemented to verify the performance of the AMIDIF compared with the state-of-the-art methods including spectral kurtosis (SK), multi-scale average combination different morphological filter (ACDIF) and multi-scale morphology gradient product operation (MGPO). The results prove that the AMIDIF has excellent performance in extracting transient features for rotating machines fault diagnosis.
Junchao Guo; Zhanqun Shi; Haiyang Li; Dong Zhen; Fengshou Gu; Andrew D. Ball. Transient impulses enhancement based on adaptive multi-scale improved differential filter and its application in rotating machines fault diagnosis. ISA Transactions 2021, 1 .
AMA StyleJunchao Guo, Zhanqun Shi, Haiyang Li, Dong Zhen, Fengshou Gu, Andrew D. Ball. Transient impulses enhancement based on adaptive multi-scale improved differential filter and its application in rotating machines fault diagnosis. ISA Transactions. 2021; ():1.
Chicago/Turabian StyleJunchao Guo; Zhanqun Shi; Haiyang Li; Dong Zhen; Fengshou Gu; Andrew D. Ball. 2021. "Transient impulses enhancement based on adaptive multi-scale improved differential filter and its application in rotating machines fault diagnosis." ISA Transactions , no. : 1.
Tooth tip chipping that the top of a tooth breaks away from lower portion is one of the common gear faults which will affect the dynamic characteristics of gears. In the assumption of plane-fracture tooth tip chipping, the tooth surface fracture line tilted configuration relatively to the gear axis is a curve, which is simplified to a straight in previous studies. However, such simplification will cause an estimate error of mesh stiffness. Therefore, to accurately estimate the mesh stiffness, failure models of tooth tip chipping for both internal gear and external gear are established by means of analytic geometry method in this paper. The fracture curve Eq. is derived by using analytic geometry method. And the mesh stiffness of the fault gear is derived via potential energy method and verified by the finite element method (FEM). The influence of different fault severity on the mesh stiffness is analyzed. The analysis results show that the proposed model can accurately evaluate the mesh stiffness of gears with tooth tip chipping and the modeling method can also be applied to other fault types.
Yinghui Liu; Zhanqun Shi; Guoji Shen; Dong Zhen; Feiyue Wang; Fengshou Gu. Evaluation model of mesh stiffness for spur gear with tooth tip chipping fault. Mechanism and Machine Theory 2021, 158, 104238 .
AMA StyleYinghui Liu, Zhanqun Shi, Guoji Shen, Dong Zhen, Feiyue Wang, Fengshou Gu. Evaluation model of mesh stiffness for spur gear with tooth tip chipping fault. Mechanism and Machine Theory. 2021; 158 ():104238.
Chicago/Turabian StyleYinghui Liu; Zhanqun Shi; Guoji Shen; Dong Zhen; Feiyue Wang; Fengshou Gu. 2021. "Evaluation model of mesh stiffness for spur gear with tooth tip chipping fault." Mechanism and Machine Theory 158, no. : 104238.
The fault detection and diagnosis (FDD) along with condition monitoring (CM) and of rotating machinery (RM) have critical importance for early diagnosis to prevent severe damage of infrastructure in industrial environments. Importantly, valuable industrial equipment needs continuous monitoring to enhance the safety, reliability, and availability and to decrease the cost of maintenance of modern industrial systems and applications. However, induction motor (IM) has been extensively used in several industrial processes because it is cheap, reliable, and robust. Rolling bearings are considered to be the main component of IM. Undoubtedly, any failure of this basic component can lead to a serious breakdown of IM and for whole industrial system. Thus, many current methods based on different techniques are employed as a fault prognosis and diagnosis of rolling elements bearing of IM. Moreover, these techniques include signal/image processing, intelligent diagnostics, data fusion, data mining, and expert systems for time and frequency as well as time-frequency domains. Artificial intelligence (AI) techniques have proven their significance in every field of digital technology. Industrial machines, automation, and processes are the net frontiers of AI adaptation. There are quite developed literatures that have been approaching the issues using signals and data processing techniques. However, the key contribution of this work is to present an extensive review of CM and FDD of the IM, especially for rolling elements bearings, based on artificial intelligent (AI) methods. This study highlights the advantages and performance limitations of each method. Finally, challenges and future trends are also highlighted.
Omar Alshorman; Muhammad Irfan; Nordin Saad; D. Zhen; Noman Haider; Adam Glowacz; Ahmad Alshorman. A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor. Shock and Vibration 2020, 2020, 1 -20.
AMA StyleOmar Alshorman, Muhammad Irfan, Nordin Saad, D. Zhen, Noman Haider, Adam Glowacz, Ahmad Alshorman. A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor. Shock and Vibration. 2020; 2020 ():1-20.
Chicago/Turabian StyleOmar Alshorman; Muhammad Irfan; Nordin Saad; D. Zhen; Noman Haider; Adam Glowacz; Ahmad Alshorman. 2020. "A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor." Shock and Vibration 2020, no. : 1-20.
An integrated method for fault detection of bearing using wavelet packet energy (WPE) and fast kurtogram (FK) is proposed. The method consists of three stages. Firstly, several commonly used wavelet functions were compared to select the appropriate wavelet function for the application of WPE. Then the analyzed signal is decomposed using WPE and the energy of each decomposed signal is calculated and selected for signal reconstruction. Secondly, the reconstructed signal is analyzed by FK to select the best central frequency and bandwidth for the band-pass filter. Finally, the filtered signal is processed using the squared envelope frequency spectrum and compared with the theoretical fault characteristic frequency for fault feature extraction. The procedure and performance of the proposed approach are illustrated and estimated by the simulation analysis, proving that the proposed method can effectively extract the weak transients. Moreover, the analysis results of gearbox bearing and rolling bearing cases show that the proposed method can provide more accurate fault features compared with the individual FK method.
Xiaojun Zhang; Jirui Zhu; Yaqi Wu; Dong Zhen; Minglu Zhang. Feature Extraction for Bearing Fault Detection Using Wavelet Packet Energy and Fast Kurtogram Analysis. Applied Sciences 2020, 10, 7715 .
AMA StyleXiaojun Zhang, Jirui Zhu, Yaqi Wu, Dong Zhen, Minglu Zhang. Feature Extraction for Bearing Fault Detection Using Wavelet Packet Energy and Fast Kurtogram Analysis. Applied Sciences. 2020; 10 (21):7715.
Chicago/Turabian StyleXiaojun Zhang; Jirui Zhu; Yaqi Wu; Dong Zhen; Minglu Zhang. 2020. "Feature Extraction for Bearing Fault Detection Using Wavelet Packet Energy and Fast Kurtogram Analysis." Applied Sciences 10, no. 21: 7715.
Considering fault impulse signals of rolling element bearings have the features of periodicity and easily to be immerged by background noise, a novel fault feature extraction method based on the wavelet packet transform (WPT) and the time-delay correlation demodulation analysis is proposed in this paper. Firstly, the signal-to-noise ratio (SNR) and the root-mean-square error (RMSE) are used as the criterion to select the optimal wavelet packet parameters to enhance the SNR of the vibration signal. Then, the denoised signal is reconstructed for further analysis. Finally, the fault features of the reconstructed signal are extracted using time-delay correlation demodulation analysis. The results show that the fault characteristic frequencies can be extracted with higher accuracy based on the simulation and experimental studies, respectively. It can be concluded that the proposed method has more effectiveness and feasibility for fault diagnosis of rolling element bearing with higher accuracy.
Chen Zhang; Junchao Guo; Dong Zhen; Hao Zhang; Zhanqun Shi; Fengshou Gu; Andrew Ball. Rolling Element Bearing Fault Diagnosis Based on the Wavelet Packet Transform and Time-Delay Correlation Demodulation Analysis. Blockchain Technology and Innovations in Business Processes 2020, 1195 -1203.
AMA StyleChen Zhang, Junchao Guo, Dong Zhen, Hao Zhang, Zhanqun Shi, Fengshou Gu, Andrew Ball. Rolling Element Bearing Fault Diagnosis Based on the Wavelet Packet Transform and Time-Delay Correlation Demodulation Analysis. Blockchain Technology and Innovations in Business Processes. 2020; ():1195-1203.
Chicago/Turabian StyleChen Zhang; Junchao Guo; Dong Zhen; Hao Zhang; Zhanqun Shi; Fengshou Gu; Andrew Ball. 2020. "Rolling Element Bearing Fault Diagnosis Based on the Wavelet Packet Transform and Time-Delay Correlation Demodulation Analysis." Blockchain Technology and Innovations in Business Processes , no. : 1195-1203.
Wheel flat is not only commonly unavoidable surface damage in railway wheels, it can result in possible damage and deterioration incurring high risk of running safety and high maintenance costs. Wheel flat is therefore necessary to be detected at an early stage to minimise safety hazard and maintenance work. This study explores the capacity of the vibration-based detection for high-speed train wheel flatness. A more realistic vehicle-track coupling dynamic model (a dynamic model of vehicle systems of 94 degrees of freedom with wheel flat) considering the dynamic factors of traction transmission, gear transmission and the track geometry irregularities, is established to calculate the dynamic responses of axlebox. In this paper, the proposed method is focus on processing the axle box vertical vibration caused by wheel flat in conventional time and frequency domain, as well as the envelope analysis with a band pass filter. Results demonstrate that the wheel flat can be successfully detected in a more realistic vehicle model, provide an efficient way to the wheel flat detection.
Ruichen Wang; David Crosbee; Adam Beven; Zhiwei Wang; Dong Zhen. Vibration-Based Detection of Wheel Flat on a High-Speed Train. Blockchain Technology and Innovations in Business Processes 2020, 159 -169.
AMA StyleRuichen Wang, David Crosbee, Adam Beven, Zhiwei Wang, Dong Zhen. Vibration-Based Detection of Wheel Flat on a High-Speed Train. Blockchain Technology and Innovations in Business Processes. 2020; ():159-169.
Chicago/Turabian StyleRuichen Wang; David Crosbee; Adam Beven; Zhiwei Wang; Dong Zhen. 2020. "Vibration-Based Detection of Wheel Flat on a High-Speed Train." Blockchain Technology and Innovations in Business Processes , no. : 159-169.
In this paper, the subsurface defect model is established by a finite element analysis software. Subsurface defects can be identified by the analysis of the sample’s temperature field. Size-to-depth ratio’s law is used to study the sample’s subsurface defects. Qualitative analysis of the subsurface defects can be achieved by the temperature distribution of sample’s surface. Experimental model is established according to simulation model, the reliability of the simulation results can be verified by experiments, the correctness of the simulation results can be analyzed by experimental data. The experimental results show that subsurface defect can be identified when size-to-depth ratio is greater than 2. The highest temperature region on the surface of the sample appears in the subsurface defect area. Qualitative analysis of subsurface defects can be obtained by temperature distribution.
Fan Jiang; Xiaoyu Xu; Dong Zhen; Hao Zhang; Shijie Dai; Zhanqun Shi. The Detection of Defects on Metallic Subsurface Based on Pulsed Eddy Current Thermography. Blockchain Technology and Innovations in Business Processes 2020, 1183 -1193.
AMA StyleFan Jiang, Xiaoyu Xu, Dong Zhen, Hao Zhang, Shijie Dai, Zhanqun Shi. The Detection of Defects on Metallic Subsurface Based on Pulsed Eddy Current Thermography. Blockchain Technology and Innovations in Business Processes. 2020; ():1183-1193.
Chicago/Turabian StyleFan Jiang; Xiaoyu Xu; Dong Zhen; Hao Zhang; Shijie Dai; Zhanqun Shi. 2020. "The Detection of Defects on Metallic Subsurface Based on Pulsed Eddy Current Thermography." Blockchain Technology and Innovations in Business Processes , no. : 1183-1193.
Planetary gearboxes are widely used in mechanical transmission systems due to their large transmission ratio and high transmission efficiency. In a planetary gearbox, the sun gear is usually set to float to balance the sharing of loads among planet gears. However, this floating set will result in the variation of pressure angle, overlap ratio, and meshing phase in the meshing progress and when gear faults occur, the variation will be enlarged. In the previous studies, these parameters were reduced to constant. To study the influence of the dynamic parameters on the vibration response of planetary gearboxes under different operating conditions, a new lumped-parameter model containing the time-varying pressure angle (TVPA), time-varying overlap ratio (TVOR), and time-varying meshing phase (TVMP) is established. Based on this model, the vibration response mechanism of the sun gear is analyzed. Moreover, the comparison with the previous model is made and the rule of phase modulation caused by these dynamic parameters is revealed. By comparing the dynamic responses under different loads and rotation speeds, the phase modulation is studied in detail. Finally, the sun gear fault is introduced, and the phase modulation is analyzed in different fault degrees. This study can provide theoretical reference for the condition monitoring and fault diagnosis of planetary gearbox based on vibration analysis.
Yinghui Liu; Dong Zhen; Huibo Zhang; Hao Zhang; Zhanqun Shi; Fengshou Gu. Vibration Response of the Planetary Gears with a Float Sun Gear and Influences of the Dynamic Parameters. Shock and Vibration 2020, 2020, 1 -17.
AMA StyleYinghui Liu, Dong Zhen, Huibo Zhang, Hao Zhang, Zhanqun Shi, Fengshou Gu. Vibration Response of the Planetary Gears with a Float Sun Gear and Influences of the Dynamic Parameters. Shock and Vibration. 2020; 2020 ():1-17.
Chicago/Turabian StyleYinghui Liu; Dong Zhen; Huibo Zhang; Hao Zhang; Zhanqun Shi; Fengshou Gu. 2020. "Vibration Response of the Planetary Gears with a Float Sun Gear and Influences of the Dynamic Parameters." Shock and Vibration 2020, no. : 1-17.
In the motor current signal, the characteristic frequency of broken rotor bar (BRB) fault is modulated by the supply frequency and it decreases with the decrease of the load, resulting it to be easily buried under light load conditions. Teager-Kaiser energy operator (TKEO) has shown better performance to detect BRB faults than classical methods, such as envelope and spectral analysis. However, the original definition of TKEO leads to its result lack of physical meanings and the causal processing in TKEO can lead to phase distortion and non-ideal filter characteristics. Therefore, this paper proposes a normalized frequency domain energy operator (FDEO) for the BRB fault diagnosis, which does not require causal processing and calculates multiple differentiations in the frequency domain with equal accuracy in one operation. Furthermore, normalized FDEO removes the influence of the supply frequency followed by spectral analysis to extract fault features. The mathematical model of induction motor under healthy and faulty condition are studied in this article. Then, the proposed approach is experimentally validated with seeded one and two BRB faults operating under various load conditions. To verify the effectiveness, the results are compared with TKEO, envelope and spectral analysis. It was found that the proposed method provides slightly obvious fault features with respect to TKEO, especially when the IMs run under light load conditions with two BRB faults.
Haiyang Li; Guojin Feng; Dong Zhen; Fengshou Gu; Andrew David Ball. A Normalized Frequency-Domain Energy Operator for Broken Rotor Bar Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement 2020, 70, 1 -10.
AMA StyleHaiyang Li, Guojin Feng, Dong Zhen, Fengshou Gu, Andrew David Ball. A Normalized Frequency-Domain Energy Operator for Broken Rotor Bar Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement. 2020; 70 (99):1-10.
Chicago/Turabian StyleHaiyang Li; Guojin Feng; Dong Zhen; Fengshou Gu; Andrew David Ball. 2020. "A Normalized Frequency-Domain Energy Operator for Broken Rotor Bar Fault Diagnosis." IEEE Transactions on Instrumentation and Measurement 70, no. 99: 1-10.
The rolling element bearings are extensively applied in rotating machines, and they are the most susceptible components in rotating machines. Early fault detection of bearings is to prevent machines from such typical failures and subsequent consequences. In this paper a detector based on Ensemble Average of Autocorrelated Envelopes (EAAE) is proposed to identify the early occurrence faults in rolling element bearings, of which the fault induced vibration signals are inevitably contaminated or masked by both additive background noise and random phase noise (or slippage between bearing components). To enhance the cyclostationary characteristics for fault detection, it utilizes the phase synchronization property of autocorrelation signals for aligning the cyclostationary signals in the lag domain to achieve an effective ensemble average which allows both types of random influences to be suppressed significantly. As a result, this detector shows very high performance of robustness in extracting the local fault signatures, which is verified by simulation signals and experimental investigations and benchmarked by the recent milestone method of Spectral Correlation (SC).
Yuandong Xu; Dong Zhen; James Xi Gu; Khalid Rabeyee; Fulei Chu; Fengshou Gu; Andrew D. Ball. Autocorrelated Envelopes for early fault detection of rolling bearings. Mechanical Systems and Signal Processing 2020, 146, 106990 .
AMA StyleYuandong Xu, Dong Zhen, James Xi Gu, Khalid Rabeyee, Fulei Chu, Fengshou Gu, Andrew D. Ball. Autocorrelated Envelopes for early fault detection of rolling bearings. Mechanical Systems and Signal Processing. 2020; 146 ():106990.
Chicago/Turabian StyleYuandong Xu; Dong Zhen; James Xi Gu; Khalid Rabeyee; Fulei Chu; Fengshou Gu; Andrew D. Ball. 2020. "Autocorrelated Envelopes for early fault detection of rolling bearings." Mechanical Systems and Signal Processing 146, no. : 106990.
The vibration of a planetary gearbox (PG) is complex and mutually modulated, which makes the weak features of incipient fault difficult to detect. To target this problem, a novel method, based on an adaptive order bispectrum slice (AOBS) and the fault characteristics energy ratio (FCER), is proposed. The order bispectrum (OB) method has shown its effectiveness in the feature extraction of bearings and fixed-shaft gearboxes. However, the effectiveness of the PG still needs to be explored. The FCER is developed to sum up the fault information, which is scattered by mutual modulation. In this method, the raw vibration signal is firstly converted to that in the angle domain. Secondly, the characteristic slice of AOBS is extracted. Different from the conventional OB method, the AOBS is extracted by searching for a characteristic carrier frequency adaptively in the sensitive range of signal coupling. Finally, the FCER is summed up and calculated from the fault features that were dispersed in the characteristic slice. Experimental data was processed, using both the AOBS-FCER method, and the method that combines order spectrum analysis with sideband energy ratio (OSA-SER), respectively. Results indicated that the new method is effective in incipient fault feature extraction, compared with the methods of OB and OSA-SER.
Zhaoyang Shen; Zhanqun Shi; Dong Zhen; Hao Zhang; Fengshou Gu. Fault Diagnosis of Planetary Gearbox Based on Adaptive Order Bispectrum Slice and Fault Characteristics Energy Ratio Analysis. Sensors 2020, 20, 2433 .
AMA StyleZhaoyang Shen, Zhanqun Shi, Dong Zhen, Hao Zhang, Fengshou Gu. Fault Diagnosis of Planetary Gearbox Based on Adaptive Order Bispectrum Slice and Fault Characteristics Energy Ratio Analysis. Sensors. 2020; 20 (8):2433.
Chicago/Turabian StyleZhaoyang Shen; Zhanqun Shi; Dong Zhen; Hao Zhang; Fengshou Gu. 2020. "Fault Diagnosis of Planetary Gearbox Based on Adaptive Order Bispectrum Slice and Fault Characteristics Energy Ratio Analysis." Sensors 20, no. 8: 2433.
Transient impulses are important information for machinery fault diagnosis. However, the transient features contained in the vibration signals generated by planetary gearboxes are usually immersed by a large amount of background noise and harmonic components. Even mathematical morphology (MM) is an excellent anti-noise signal processing method that can directly extract the geometry of impulse features in the time domain, but the four basic operators of MM can only extract one-way impulses while cannot extract the bidirectional impulses effectively at the same time. To accurately extract the impulse feature information, a novel method for fault detection of planetary gearbox based on an enhanced average (EAVG) filter and modulated signal bispectrum (MSB) is proposed. Firstly, the properties of the extracted impulses based on the four basic operators of MM will be divided into two categories of enhanced average operators. The four EAVG filters consist of the average weighted combination of enhanced average operators, and then the best EAVG filter is selected based on correlation coefficient to implement on the original vibration signal. It allows EAVG filter to extract positive and negative impulses of vibration signal, thereby improving the accuracy of planetary gearbox fault detection. Subsequently, the performance of the EAVG filter is influenced by the length of its structural element (SE), which is adaptively determined using an indicator based kurtosis. Then, the EAVG filter selects the optimal SE length to eliminate the interference of background noise and harmonic components to enhance the impulse components of the vibration signal. However, the nonlinear modulation components that are related to the fault types and severities are not extracted exactly and still remained in the filtered signal by EAVG. Finally, the MSB is utilized to the EAVG filtered signal to decompose the modulated components and extract the fault features. The advantages of EAVG over average (AVG) filter are clarified in the simulation study. In addition, the EAVG-MSB is validated by analyzing the vibration signals of planetary gearboxes with sun gear chipped tooth, sun gear misalignment and bearing inner race fault. The results indicate that the EAVG-MSB is effective and accurate in feature extraction compared with the combination morphological filter-hat transform (CMFH) and average combination difference morphological filter (ACDIF), and the feasibility of the EAVG-MSB are proved for planetary gearbox condition monitoring and fault diagnosis.
Junchao Guo; Dong Zhen; Haiyang Li; Zhanqun Shi; Fengshou Gu; Andrew D. Ball. Fault detection for planetary gearbox based on an enhanced average filter and modulation signal bispectrum analysis. ISA Transactions 2020, 101, 408 -420.
AMA StyleJunchao Guo, Dong Zhen, Haiyang Li, Zhanqun Shi, Fengshou Gu, Andrew D. Ball. Fault detection for planetary gearbox based on an enhanced average filter and modulation signal bispectrum analysis. ISA Transactions. 2020; 101 ():408-420.
Chicago/Turabian StyleJunchao Guo; Dong Zhen; Haiyang Li; Zhanqun Shi; Fengshou Gu; Andrew D. Ball. 2020. "Fault detection for planetary gearbox based on an enhanced average filter and modulation signal bispectrum analysis." ISA Transactions 101, no. : 408-420.
The dynamic coefficients identification of journal bearings is essential for instability analysis of rotation machinery. Aiming at the measured displacement of a single location, an improvement method associated with the Kalman filter is proposed to estimate the bearing dynamic coefficients. Firstly, a finite element model of the flexible rotor-bearing system was established and then modified by the modal test. Secondly, the model-based identification procedure was derived, in which the displacements of the shaft at bearings locations were estimated by the Kalman filter algorithm to identify the dynamic coefficients. Finally, considering the effect of the different process noise covariance, the corresponding numerical simulations were carried out to validate the preliminary accuracy. Furthermore, experimental tests were conducted to confirm the practicality, where the real stiffness and damping were comprehensively identified under the different operating conditions. The results show that the proposed method is not only highly accurate, but also stable under different measured locations. Compared with the conventional method, this study presents a more than high practicality approach to identify dynamic coefficients, including under the resonance condition. With high efficiency, it can be extended to predict the dynamic behaviour of rotor-bearing systems.
Yang Kang; Zhanqun Shi; Hao Zhang; Dong Zhen; Fengshou Gu. A Novel Method for the Dynamic Coefficients Identification of Journal Bearings Using Kalman Filter. Sensors 2020, 20, 565 .
AMA StyleYang Kang, Zhanqun Shi, Hao Zhang, Dong Zhen, Fengshou Gu. A Novel Method for the Dynamic Coefficients Identification of Journal Bearings Using Kalman Filter. Sensors. 2020; 20 (2):565.
Chicago/Turabian StyleYang Kang; Zhanqun Shi; Hao Zhang; Dong Zhen; Fengshou Gu. 2020. "A Novel Method for the Dynamic Coefficients Identification of Journal Bearings Using Kalman Filter." Sensors 20, no. 2: 565.
Broken rotor bar (BRB) faults are one of the most common faults in induction motors (IM). One or more broken bars can reduce the performance and efficiency of the IM and hence waste the electrical power and decrease the reliability of the whole mechanical system. This paper proposes an effective fault diagnosis method using the Teager–Kaiser energy operator (TKEO) for BRB faults detection based on the motor current signal analysis (MCSA). The TKEO is investigated and applied to remove the main supply component of the motor current for accurate fault feature extraction, especially for an IM operating at low load with low slip. Through sensing the estimation of the instantaneous amplitude (IA) and instantaneous frequency (IF) of the motor current signal using TKEO, the fault characteristic frequencies can be enhanced and extracted for the accurate detection of BRB fault severities under different operating conditions. The proposed method has been validated by simulation and experimental studies that tested the IMs with different BRB fault severities to consider the effectiveness of the proposed method. The obtained results are compared with those obtained using the conventional envelope analysis methods and showed that the proposed method provides more accurate fault diagnosis results and can distinguish the BRB fault types and severities effectively, especially for operating conditions with low loads.
Haiyang Li; Zuolu Wang; Dong Zhen; Fengshou Gu; Andrew Ball. Modulation Sideband Separation Using the Teager–Kaiser Energy Operator for Rotor Fault Diagnostics of Induction Motors. Energies 2019, 12, 4437 .
AMA StyleHaiyang Li, Zuolu Wang, Dong Zhen, Fengshou Gu, Andrew Ball. Modulation Sideband Separation Using the Teager–Kaiser Energy Operator for Rotor Fault Diagnostics of Induction Motors. Energies. 2019; 12 (23):4437.
Chicago/Turabian StyleHaiyang Li; Zuolu Wang; Dong Zhen; Fengshou Gu; Andrew Ball. 2019. "Modulation Sideband Separation Using the Teager–Kaiser Energy Operator for Rotor Fault Diagnostics of Induction Motors." Energies 12, no. 23: 4437.
This paper investigated the nonlinear vibrations of an uncertain overhung rotor system with rub-impact fault. As the clearance of the rotor and stator is getting smaller, contact between them often occurs at high rotation speeds. Meanwhile, inherent uncertainties in the rubbing can be introduced for a variety of reasons, and they are typically restricted to small-sample variables. It is important to gain a robust understanding of the dynamics of such a system under non-probabilistic uncertainties. A non-intrusive uncertainty quantification scheme, coupled with the Runge-Kutta method, was used to study the effects of the rub-impact related interval uncertainties on the dynamical response individually and simultaneously, including the uncertainties in the contact stiffness, clearance, and friction coefficient. Moreover, the numerical validation of the developed analysis method was verified through comparisons with the scanning approach. The results obtained provide some guidance for investigating the uncertain dynamics of rubbing rotors and diagnosing the rub-impact fault under non-random uncertainty.
Chao Fu; Dong Zhen; Yongfeng Yang; Fengshou Gu; Andrew Ball. Effects of Bounded Uncertainties on the Dynamic Characteristics of an Overhung Rotor System with Rubbing Fault. Energies 2019, 12, 4365 .
AMA StyleChao Fu, Dong Zhen, Yongfeng Yang, Fengshou Gu, Andrew Ball. Effects of Bounded Uncertainties on the Dynamic Characteristics of an Overhung Rotor System with Rubbing Fault. Energies. 2019; 12 (22):4365.
Chicago/Turabian StyleChao Fu; Dong Zhen; Yongfeng Yang; Fengshou Gu; Andrew Ball. 2019. "Effects of Bounded Uncertainties on the Dynamic Characteristics of an Overhung Rotor System with Rubbing Fault." Energies 12, no. 22: 4365.
Induction motors (IMs) are widely used in many manufacturing processes and industrial applications. The harsh work environment, long-time enduring, and overloads mean that it is subjected to broken rotor bar (BRB) faults. The vibration signal of IMs with BRB faults consists of the reliable modulation information used for fault diagnosis. Cyclostationary analysis has been found to be effective in identifying and extracting fault feature. The estimators of cyclic modulation spectrum (CMS) and fast spectral correlation (FSC) based on the short-time fourier transform (STFT) have higher cyclic frequency resolution, which has proven efficient in demodulating second order cyclostationary (CS2) signals. However, these two estimators have limitations of processing the maximum cyclic frequency αmax that is smaller than Fs/2 (Fs is the sampling frequency) according to Nyquist’s Theorem. In addition, they have lower carrier frequency resolution due to the fixed window size used in STFT. In order to resolve the initial shortcomings of the CMS and FSC methods, in this paper, we extended the analysis of CMS algorithm based on the continuous wavelet transform (CWT), which enlarged the maximum cyclic frequency range to Fs/2 and provides higher carrier frequency resolution because the CWT has the advantage of multi-resolution analysis. The reliability and applicability of the proposed method for fault components localization were validated by CS2 simulation signals. Compared to CMS and FSC methods, the proposed approach shows better performance by analyzing vibration signals between healthy motor and faulty motor with one BRB fault under 0%, 20%, 40%, and 80% load conditions.
Dong Zhen; Zuolu Wang; Haiyang Li; Hao Zhang; Jie Yang; Fengshou Gu. An Improved Cyclic Modulation Spectral Analysis Based on the CWT and Its Application on Broken Rotor Bar Fault Diagnosis for Induction Motors. Applied Sciences 2019, 9, 3902 .
AMA StyleDong Zhen, Zuolu Wang, Haiyang Li, Hao Zhang, Jie Yang, Fengshou Gu. An Improved Cyclic Modulation Spectral Analysis Based on the CWT and Its Application on Broken Rotor Bar Fault Diagnosis for Induction Motors. Applied Sciences. 2019; 9 (18):3902.
Chicago/Turabian StyleDong Zhen; Zuolu Wang; Haiyang Li; Hao Zhang; Jie Yang; Fengshou Gu. 2019. "An Improved Cyclic Modulation Spectral Analysis Based on the CWT and Its Application on Broken Rotor Bar Fault Diagnosis for Induction Motors." Applied Sciences 9, no. 18: 3902.
To realize the accurate fault detection of rolling element bearings, a novel fault detection method based on non-stationary vibration signal analysis using weighted average ensemble empirical mode decomposition (WAEEMD) and modulation signal bispectrum (MSB) is proposed in this paper. Bispectrum is a third-order statistic, which can not only effectively suppress Gaussian noise, but also help identify phase coupling. However, it cannot effectively decompose the modulation components which are inherent in vibration signals. To alleviate this issue, MSB based on the modulation characteristics of the signals is developed for demodulation and noise reduction. Still, the direct application of MSB has some interfering frequency components when extracting fault features from non-stationary signals. Ensemble empirical mode decomposition (EEMD) is an advanced nonlinear and non-stationary signal processing approach that can decompose the signal into a list of stationary intrinsic mode functions (IMFs). The proposed method takes advantage of WAEEMD and MSB for bearing fault diagnosis based on vibration signature analysis. Firstly, the vibration signal is decomposed into IMFs with a different frequency band using EEMD. Then, the IMFs are reconstructed into a new signal by the weighted average method, called WAEEMD, based on Teager energy kurtosis (TEK). Finally, MSB is applied to decompose the modulated components in the reconstructed signal and extract the fault characteristic frequencies for fault detection. Furthermore, the efficiency and performance of the proposed WAEEMD-MSB approach is demonstrated on the fault diagnosis for a motor bearing outer race fault and a gearbox bearing inner race fault. The experimental results verify that the WAEEMD-MSB has superior performance over conventional MSB and EEMD-MSB in extracting fault features and has precise and effective advantages for rolling element bearing fault detection.
Dong Zhen; Junchao Guo; Yuandong Xu; Hao Zhang; Fengshou Gu. A Novel Fault Detection Method for Rolling Bearings Based on Non-Stationary Vibration Signature Analysis. Sensors 2019, 19, 3994 .
AMA StyleDong Zhen, Junchao Guo, Yuandong Xu, Hao Zhang, Fengshou Gu. A Novel Fault Detection Method for Rolling Bearings Based on Non-Stationary Vibration Signature Analysis. Sensors. 2019; 19 (18):3994.
Chicago/Turabian StyleDong Zhen; Junchao Guo; Yuandong Xu; Hao Zhang; Fengshou Gu. 2019. "A Novel Fault Detection Method for Rolling Bearings Based on Non-Stationary Vibration Signature Analysis." Sensors 19, no. 18: 3994.
Induction motors (IMs) play an essential role in the field of various industrial applications. Long-time service and tough working situations make IMs become prone to a broken rotor bar (BRB) that is one of the major causes of IMs faults. Hence, the continuous condition monitoring of BRB faults demands a computationally efficient and accurate signal diagnosis technique. The advantage of high reliability and wide applicability in condition monitoring and fault diagnosis based on vibration signature analysis results in an improved cyclic modulation spectrum (CMS), which is one of the cyclic spectral analysis algorithms. CMS is proposed in this paper for the detection and identification of BRB faults in IMs at a steady-state operation based on a vibration signature analysis. The application of CMS is based on the short-time Fourier transform (STFT) and the improved CMS approach is attributed to the optimization of STFT. The optimal window is selected to improve the accuracy for identifying the BRB fault types and severities. The appropriate window length and step size are optimized based on the selected window function to receive a better calculation benefit through simulation and experimental analysis. Compared to other estimators, the improved CMS method provides better fault detectability results by analyzing vertical vibration signatures of a healthy motor, and damaged motors with 1 BRB and 2 BRBs under 0%, 20%, 40%, 60%, and 80% load conditions. Both synthetic and experimental investigations demonstrate the proposed methodology can significantly reduce computational costs and identify the BRB fault types and severities effectively.
Zuolu Wang; Jie Yang; Haiyang Li; Dong Zhen; Yuandong Xu; Fengshou Gu. Fault Identification of Broken Rotor Bars in Induction Motors Using an Improved Cyclic Modulation Spectral Analysis. Energies 2019, 12, 3279 .
AMA StyleZuolu Wang, Jie Yang, Haiyang Li, Dong Zhen, Yuandong Xu, Fengshou Gu. Fault Identification of Broken Rotor Bars in Induction Motors Using an Improved Cyclic Modulation Spectral Analysis. Energies. 2019; 12 (17):3279.
Chicago/Turabian StyleZuolu Wang; Jie Yang; Haiyang Li; Dong Zhen; Yuandong Xu; Fengshou Gu. 2019. "Fault Identification of Broken Rotor Bars in Induction Motors Using an Improved Cyclic Modulation Spectral Analysis." Energies 12, no. 17: 3279.