Pub Date : 2021-10-15DOI: 10.1109/PHM-Nanjing52125.2021.9613125
Zhengping Li, Kaiqiang Liu, Lei Xiao
At present, most of the existing bearing fault diagnosis methods focus on a single working condition. However, it is far from the complex working condition with changeable motor speed, environmental noise interference and the weakness of early feature in the real industrial applications. Therefore, it is very significant to determine appropriate features for intelligent fault diagnosis of rolling element bearings (REBs) under complex working conditions. To solve this problem, an intelligent diagnosis method of bearing faults based on spectrum kurtosis (SK), envelope spectrum (ES) and convolutional neural net (CNN) is proposed in this paper under variable rotational speed and multiple fault states. In this method, SK and bandpass filtering are firstly used to improve the signal-to-noise rate (SNR) of fault from the original vibration signals. Then the rich information of fault characteristic frequencies related to the rotating speed is extracted by ES analysis. Subsequently, a CNN model is built to identify bearing defects by automatically extracting these representative features. Four experiments are performed on the Case Western Reserve University (CWRU) bearing dataset to demonstrate the effectiveness of this method. By comparing experiment results with others, the superiority and effectiveness of this method are illustrated.
{"title":"Bearing Intelligent Fault Diagnosis Under Complex Working Condition Based on SK-ES-CNN","authors":"Zhengping Li, Kaiqiang Liu, Lei Xiao","doi":"10.1109/PHM-Nanjing52125.2021.9613125","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9613125","url":null,"abstract":"At present, most of the existing bearing fault diagnosis methods focus on a single working condition. However, it is far from the complex working condition with changeable motor speed, environmental noise interference and the weakness of early feature in the real industrial applications. Therefore, it is very significant to determine appropriate features for intelligent fault diagnosis of rolling element bearings (REBs) under complex working conditions. To solve this problem, an intelligent diagnosis method of bearing faults based on spectrum kurtosis (SK), envelope spectrum (ES) and convolutional neural net (CNN) is proposed in this paper under variable rotational speed and multiple fault states. In this method, SK and bandpass filtering are firstly used to improve the signal-to-noise rate (SNR) of fault from the original vibration signals. Then the rich information of fault characteristic frequencies related to the rotating speed is extracted by ES analysis. Subsequently, a CNN model is built to identify bearing defects by automatically extracting these representative features. Four experiments are performed on the Case Western Reserve University (CWRU) bearing dataset to demonstrate the effectiveness of this method. By comparing experiment results with others, the superiority and effectiveness of this method are illustrated.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130661764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-15DOI: 10.1109/PHM-Nanjing52125.2021.9612865
Huang Jiaxing, Sun Meng, Jing Bo, Liu Jingyuan, Cao Xin
Aiming at the problem of inaccurate description of two-phase degradation of products, a two-phase nonlinear Wiener model was established, and a residual life prediction method was proposed. Firstly, considering the random effect of the degenerate change-point, a two-phase nonlinear Wiener degradation model is established by using the normal distribution to describe the drift parameters of each phase. Secondly, based on Bayesian theory, the posteriori distribution of model parameters is derived, and the MHGS method is proposed to estimate the parameters of the two-phase degradation model. Then, a method to determine the degradation stage was proposed, based on DIC criterion. Combined with the state-space model and Kalman filter, the online updating process and residual life probability distribution of the two-phase degradation model were deduced. Finally, the proposed model and method are validated by solder joint degradation data. The results show that the proposed method can accurately estimate the model parameters and predict the residual life. Compared with the two-phase model of linear Wiener process, the two-phase model of nonlinear Wiener process proposed in this paper has higher prediction accuracy.
{"title":"Two-Phase Degradation Modeling and Residual Life Prediction Based on Nonlinear Wiener Process","authors":"Huang Jiaxing, Sun Meng, Jing Bo, Liu Jingyuan, Cao Xin","doi":"10.1109/PHM-Nanjing52125.2021.9612865","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612865","url":null,"abstract":"Aiming at the problem of inaccurate description of two-phase degradation of products, a two-phase nonlinear Wiener model was established, and a residual life prediction method was proposed. Firstly, considering the random effect of the degenerate change-point, a two-phase nonlinear Wiener degradation model is established by using the normal distribution to describe the drift parameters of each phase. Secondly, based on Bayesian theory, the posteriori distribution of model parameters is derived, and the MHGS method is proposed to estimate the parameters of the two-phase degradation model. Then, a method to determine the degradation stage was proposed, based on DIC criterion. Combined with the state-space model and Kalman filter, the online updating process and residual life probability distribution of the two-phase degradation model were deduced. Finally, the proposed model and method are validated by solder joint degradation data. The results show that the proposed method can accurately estimate the model parameters and predict the residual life. Compared with the two-phase model of linear Wiener process, the two-phase model of nonlinear Wiener process proposed in this paper has higher prediction accuracy.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123876954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-15DOI: 10.1109/PHM-Nanjing52125.2021.9613056
Baiteng Ma, Xuegong Zhao, Lei Xiao
The prediction of remaining useful life (RUL) plays an important role in prognostics and health management (PHM) to improve the reliability of machines and reduce the cycle cost of mechanical systems. In recent years, deep learning (DL) for RUL prediction has become increasingly popular with the dramatic increase in computational power and has yielded a large number of results in research. However, most DL learning prediction frameworks tend to provide only a point estimate, but there is relatively less research on the uncertainty of the prediction and the confidence interval of the prediction results. This paper proposes a variational inferential Bayesian method to enhance the study of prediction result uncertainty, consequently, the output of prediction result changes from a point estimate to a confidence interval output. To improve the prediction accuracy, the feature are extracted and reconstructed, which make the feature degradation more recognizable. Furthermore, an attention mechanism is considered to improve the performance of RUL prediction by assigning weights to the input features. The effectiveness of our proposed method is validated with a publicly available dataset and compared with the-state-of-the-art methods.
{"title":"Remaining Useful Life Estimation Based On Feature Reconstruction And Variational Bayesian Inferences","authors":"Baiteng Ma, Xuegong Zhao, Lei Xiao","doi":"10.1109/PHM-Nanjing52125.2021.9613056","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9613056","url":null,"abstract":"The prediction of remaining useful life (RUL) plays an important role in prognostics and health management (PHM) to improve the reliability of machines and reduce the cycle cost of mechanical systems. In recent years, deep learning (DL) for RUL prediction has become increasingly popular with the dramatic increase in computational power and has yielded a large number of results in research. However, most DL learning prediction frameworks tend to provide only a point estimate, but there is relatively less research on the uncertainty of the prediction and the confidence interval of the prediction results. This paper proposes a variational inferential Bayesian method to enhance the study of prediction result uncertainty, consequently, the output of prediction result changes from a point estimate to a confidence interval output. To improve the prediction accuracy, the feature are extracted and reconstructed, which make the feature degradation more recognizable. Furthermore, an attention mechanism is considered to improve the performance of RUL prediction by assigning weights to the input features. The effectiveness of our proposed method is validated with a publicly available dataset and compared with the-state-of-the-art methods.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123380162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-15DOI: 10.1109/PHM-Nanjing52125.2021.9613015
L. Luo, Xianghua Huang, Tianhong Zhang
Sensor is the foundation of aero-engine health management. In turboprop engine measurement system, in order to effectively solve the problems of multi-sensor installation space and unbalance of phonic wheel rotor, an integrated measurement system of propeller pitch, phase angle and speed of turboprop engine based on novel phonic wheel was studied. A novel phonic wheel structure with multiple regular teeth and a set of symmetrically marker helical teeth is proposed, the sensor waveform is modulated into square wave through the signal processing module, and the propeller pitch, phase angle and speed of propeller can be obtained by analyzing the edge time of the square wave. Using numerical simulation of COMSOL, the influence of different phonic wheel structures on voltage waveform and measurement accuracy were studied. Simulation results show that the error curves of propeller pitch and phase angle show good linear characteristics in the non-extreme ranges of difference angle of teeth. Under the condition of no error compensation, the measurement errors of propeller pitch, phase angle and speed can be maintained within -0.35mm$sim 0.05mm, 0^{circ}sim 0.11^{circ}$and $0.01sim 0.01$r/min separately, which can meet the control accuracy requirements of turboprop engine.
{"title":"Integrated Measurement System of Three Parameters of Turboprop Engine Based on Novel Phonic Wheel","authors":"L. Luo, Xianghua Huang, Tianhong Zhang","doi":"10.1109/PHM-Nanjing52125.2021.9613015","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9613015","url":null,"abstract":"Sensor is the foundation of aero-engine health management. In turboprop engine measurement system, in order to effectively solve the problems of multi-sensor installation space and unbalance of phonic wheel rotor, an integrated measurement system of propeller pitch, phase angle and speed of turboprop engine based on novel phonic wheel was studied. A novel phonic wheel structure with multiple regular teeth and a set of symmetrically marker helical teeth is proposed, the sensor waveform is modulated into square wave through the signal processing module, and the propeller pitch, phase angle and speed of propeller can be obtained by analyzing the edge time of the square wave. Using numerical simulation of COMSOL, the influence of different phonic wheel structures on voltage waveform and measurement accuracy were studied. Simulation results show that the error curves of propeller pitch and phase angle show good linear characteristics in the non-extreme ranges of difference angle of teeth. Under the condition of no error compensation, the measurement errors of propeller pitch, phase angle and speed can be maintained within -0.35mm$sim 0.05mm, 0^{circ}sim 0.11^{circ}$and $0.01sim 0.01$r/min separately, which can meet the control accuracy requirements of turboprop engine.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121162424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-15DOI: 10.1109/PHM-Nanjing52125.2021.9612761
Wansheng Yang, Junbin Chen, Zhuyun Chen, Yixiao Liao, Weihua Li
Existing data-driven machinery fault diagnosis methods can obtain high diagnosis accuracy under the condition of abundant labeled data. However, in the actual industrial environment, complete and high-quality training data may often be distributed on multiple mechanical equipment of different regions or institutions, so-called an isolated data island problem. It is often difficult to integrate and utilize these datasets due to limitation of legal regulations or interest conflict, such as privacy protection, security risk and industry competition. Therefore, how to effectively use the separated data of multiple participants to jointly train a reliable intelligent fault diagnosis model is an urgent challenge. To address this problem, a federated transfer learning method based on averaging shared layers for bearing fault diagnosis is proposed in this study. A server-clients architecture with multiple deep transfer networks is constructed to jointly learn the global features from isolated datasets. Then, a modified federated averaging method based on shared layers is adopted to implement federated averaging of distributed feature layers from different diagnosis models, and personalized layers are updated locally. Three different bearing datasets collected by different devices are used for experimental verification. Compared with the current popular federated learning schemes, the experiment results demonstrate the effectiveness and superiority of the proposed method.
{"title":"Federated Transfer Learning for Bearing Fault Diagnosis Based on Averaging Shared Layers","authors":"Wansheng Yang, Junbin Chen, Zhuyun Chen, Yixiao Liao, Weihua Li","doi":"10.1109/PHM-Nanjing52125.2021.9612761","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612761","url":null,"abstract":"Existing data-driven machinery fault diagnosis methods can obtain high diagnosis accuracy under the condition of abundant labeled data. However, in the actual industrial environment, complete and high-quality training data may often be distributed on multiple mechanical equipment of different regions or institutions, so-called an isolated data island problem. It is often difficult to integrate and utilize these datasets due to limitation of legal regulations or interest conflict, such as privacy protection, security risk and industry competition. Therefore, how to effectively use the separated data of multiple participants to jointly train a reliable intelligent fault diagnosis model is an urgent challenge. To address this problem, a federated transfer learning method based on averaging shared layers for bearing fault diagnosis is proposed in this study. A server-clients architecture with multiple deep transfer networks is constructed to jointly learn the global features from isolated datasets. Then, a modified federated averaging method based on shared layers is adopted to implement federated averaging of distributed feature layers from different diagnosis models, and personalized layers are updated locally. Three different bearing datasets collected by different devices are used for experimental verification. Compared with the current popular federated learning schemes, the experiment results demonstrate the effectiveness and superiority of the proposed method.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114220182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-15DOI: 10.1109/PHM-Nanjing52125.2021.9613006
J. Lulu, Tao Yourui, Wang Jia
Vibration signal-based analysis is widely used in fault diagnosis and reliability evaluation for electromechanical transmission system. Due to the structural design of system, the service environment, its accuracy requirements and other factors, it is difficult to collect vibration signals for condition monitoring in some cases. As a result, the Motor Current Signature Analysis (MCSA) now develops rapidly because it can minimize the damage to the mechanical system and save economic costs while maintaining the accuracy of condition monitoring. However, the fault information contained in the current signal is weak and easily omitted. It is particularly important to effectively reduce the noise of the original signal. In addition, most of the existing researches often used the current signal to analyse the fault of the reducer, the method for predicting the remaining useful life (RUL) of the reducer is limited. In this study, a life prediction framework is proposed based on MCSA for the harmonic reducer. Maximum Correlated Kurtosis Deconvolution (MCKD) and Completed Ensemble Empirical Mode Decomposition (CEEMD) are combined to de-noise and decompose the original current signal to obtain Intrinsic Mode Function (IMF). Then effective IMF components are extracted and dimensioned in multiple domains, the degradation index of the harmonic reducer is constructed, and the degradation stage of the entire life cycle is divided. BAS optimization algorithm is used to improve the accuracy and efficiency of BP neural network model so as to predict the RUL.
{"title":"Remaining Useful Life Prediction for Reducer of Industrial Robots Based on MCSA","authors":"J. Lulu, Tao Yourui, Wang Jia","doi":"10.1109/PHM-Nanjing52125.2021.9613006","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9613006","url":null,"abstract":"Vibration signal-based analysis is widely used in fault diagnosis and reliability evaluation for electromechanical transmission system. Due to the structural design of system, the service environment, its accuracy requirements and other factors, it is difficult to collect vibration signals for condition monitoring in some cases. As a result, the Motor Current Signature Analysis (MCSA) now develops rapidly because it can minimize the damage to the mechanical system and save economic costs while maintaining the accuracy of condition monitoring. However, the fault information contained in the current signal is weak and easily omitted. It is particularly important to effectively reduce the noise of the original signal. In addition, most of the existing researches often used the current signal to analyse the fault of the reducer, the method for predicting the remaining useful life (RUL) of the reducer is limited. In this study, a life prediction framework is proposed based on MCSA for the harmonic reducer. Maximum Correlated Kurtosis Deconvolution (MCKD) and Completed Ensemble Empirical Mode Decomposition (CEEMD) are combined to de-noise and decompose the original current signal to obtain Intrinsic Mode Function (IMF). Then effective IMF components are extracted and dimensioned in multiple domains, the degradation index of the harmonic reducer is constructed, and the degradation stage of the entire life cycle is divided. BAS optimization algorithm is used to improve the accuracy and efficiency of BP neural network model so as to predict the RUL.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116251273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-15DOI: 10.1109/PHM-Nanjing52125.2021.9613112
Jiaxin Li, Kewen Wang, Chao Ni, T. Lin
Condition monitoring (CM) signals of rotating machines operating under varying speed condition typically exhibit amplitude modulation and frequency modulation characteristics. A recent study [G. Yu, T. R. Lin. Mech. Syst. Signal Process. 147 (2020) 107069] shows that multi-synchrosqueezing transform (MSST) can effectively extract the distinctive time frequency features from non-stationary signals using an iteration process in conjunction with the synchrosqueezing transform. However, the noise contained in a signal can become a serious problem as the number of iterations increases in the transform. An alternative time-frequency analysis (TFA) method blending a ridge extraction technique and a MSST transform is thus proposed in this study to overcome the noise interference problem. In this approach, the ridge extraction technique is used to extract each mono component contained in the TFA results of the MSST in turn. A noise-free time frequency representation can then be reconstructed by superimposing the time frequency distributions of all mono-components for an accurate fault diagnosis of rotating machines under varying speed condition. A peak-hold-down-sample (PHDS) algorithm is also utilized in this work to improve the computation efficiency and to avoid possible computer jamming caused by large data. electronic document is a “live” template.
{"title":"A multi-synchrosqueezing ridge extraction transform for the analysis of non-stationary multi-component signals","authors":"Jiaxin Li, Kewen Wang, Chao Ni, T. Lin","doi":"10.1109/PHM-Nanjing52125.2021.9613112","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9613112","url":null,"abstract":"Condition monitoring (CM) signals of rotating machines operating under varying speed condition typically exhibit amplitude modulation and frequency modulation characteristics. A recent study [G. Yu, T. R. Lin. Mech. Syst. Signal Process. 147 (2020) 107069] shows that multi-synchrosqueezing transform (MSST) can effectively extract the distinctive time frequency features from non-stationary signals using an iteration process in conjunction with the synchrosqueezing transform. However, the noise contained in a signal can become a serious problem as the number of iterations increases in the transform. An alternative time-frequency analysis (TFA) method blending a ridge extraction technique and a MSST transform is thus proposed in this study to overcome the noise interference problem. In this approach, the ridge extraction technique is used to extract each mono component contained in the TFA results of the MSST in turn. A noise-free time frequency representation can then be reconstructed by superimposing the time frequency distributions of all mono-components for an accurate fault diagnosis of rotating machines under varying speed condition. A peak-hold-down-sample (PHDS) algorithm is also utilized in this work to improve the computation efficiency and to avoid possible computer jamming caused by large data. electronic document is a “live” template.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"296 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114015054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-15DOI: 10.1109/PHM-Nanjing52125.2021.9612980
Fan Feilong, Cao Ming, L. Qian
While the early detection of roller bearing faults has been extensively studied, the research in this area still suffers from the following shortcomings: first, the early bearing faults are artificially implanted, hence not always revealing the true fault mode, morphology, and signal characteristics; second, since the noise reduction & early bearing fault characteristic enhancing algorithms have mainly been developed and validated using data collected under artificially implanted faults, the validity of those diagnosis algorithms is questionable. This paper tries to address those 2 issues. Bearing testing started with brand new and perfectly healthy aero-engine bearings, under multiple times of the typical aero engine load spectrum cycle. Continuously repeating this load spectrum cycle during the test naturally induces early bearing defects, providing the much needed “true failure” test data. The effectiveness of 2 typical modern fault-signal-enhancing algorithms: Maximum Correlated Kurtosis Deconvolution (MCKD) and Fast Spectral Kurtosis (FSK) method is then assessed for early aviation bearing fault, using the artificial implanted fault data and the “true failure” test data collected in this study. Finally, the optimal diagnosis method is proposed. The analysis demonstrates that the aviation bearing early fault progress can be reflected by the change trend of averaging magnitude index at bearing characteristic frequencies.
{"title":"Naturally-induced Early Aviation Bearing Fault Test and Early Bearing Fault Detection","authors":"Fan Feilong, Cao Ming, L. Qian","doi":"10.1109/PHM-Nanjing52125.2021.9612980","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612980","url":null,"abstract":"While the early detection of roller bearing faults has been extensively studied, the research in this area still suffers from the following shortcomings: first, the early bearing faults are artificially implanted, hence not always revealing the true fault mode, morphology, and signal characteristics; second, since the noise reduction & early bearing fault characteristic enhancing algorithms have mainly been developed and validated using data collected under artificially implanted faults, the validity of those diagnosis algorithms is questionable. This paper tries to address those 2 issues. Bearing testing started with brand new and perfectly healthy aero-engine bearings, under multiple times of the typical aero engine load spectrum cycle. Continuously repeating this load spectrum cycle during the test naturally induces early bearing defects, providing the much needed “true failure” test data. The effectiveness of 2 typical modern fault-signal-enhancing algorithms: Maximum Correlated Kurtosis Deconvolution (MCKD) and Fast Spectral Kurtosis (FSK) method is then assessed for early aviation bearing fault, using the artificial implanted fault data and the “true failure” test data collected in this study. Finally, the optimal diagnosis method is proposed. The analysis demonstrates that the aviation bearing early fault progress can be reflected by the change trend of averaging magnitude index at bearing characteristic frequencies.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124462755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-15DOI: 10.1109/PHM-Nanjing52125.2021.9612894
Rui Wang
The diagnosis and prognosis of fatigue cracks, which greatly influence the long-term durability of structures, is an important issue for structural health monitoring (SHM). This paper presents a study on the estimation of fatigue crack length using ultrasonic wave data. The measured signal is first denoised and truncated to extract the informative period of the signal. If a crack is detected, features are extracted to represent the distortion of the signals while reducing the influence of noise with a B-spline based method. Gaussian process regression obtained from an integration of mean and covariance functions is used for the estimation of the crack length. Real-world experiments validates the effectiveness of the proposed method.
{"title":"A B-Spline Based Gaussian Process Regression Approach for Fatigue Crack Length Estimation Using Ultrasonic Wave Data","authors":"Rui Wang","doi":"10.1109/PHM-Nanjing52125.2021.9612894","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612894","url":null,"abstract":"The diagnosis and prognosis of fatigue cracks, which greatly influence the long-term durability of structures, is an important issue for structural health monitoring (SHM). This paper presents a study on the estimation of fatigue crack length using ultrasonic wave data. The measured signal is first denoised and truncated to extract the informative period of the signal. If a crack is detected, features are extracted to represent the distortion of the signals while reducing the influence of noise with a B-spline based method. Gaussian process regression obtained from an integration of mean and covariance functions is used for the estimation of the crack length. Real-world experiments validates the effectiveness of the proposed method.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124510284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-15DOI: 10.1109/PHM-Nanjing52125.2021.9612972
Lianhua Liu, Jie Chen, Zhupeng Wen, Dianzhen Zhang, Lingling Jiao
Large slewing bearings are characterized by low rotational speed, high load bearing and long design service life, and their operating condition determines whether the rotating machinery can operate normally. Condition monitoring and prediction of degradation trends in slewing bearings have long been hot topics of research. Traditional health indicator construction and prediction methods require human extraction of features and huge amounts of state label data. To avoid these problems, a health indicator construction method is proposed that combines densely connected fully convolutional auto-encoder (DFCAE) networks with Hidden Markov Model (HMM) in this paper. The proposed method is verified by large-scale slewing bearing data from the highly accelerated life test. The proposed methodology is also compared with other common methods of constructing health indicators, and the results prove that the proposed methodology constructs better health indicators. Finally, machine learning and deep learning networks are used to predict the degradation trend of the test slewing bearing. The prediction results show that the proposed methodology can meet the prediction requirements in the actual operation of large slewing bearings.
{"title":"Densely Connected Fully Convolutional Auto-Encoder Based Slewing Bearing Degradation Trend Prediction Method","authors":"Lianhua Liu, Jie Chen, Zhupeng Wen, Dianzhen Zhang, Lingling Jiao","doi":"10.1109/PHM-Nanjing52125.2021.9612972","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612972","url":null,"abstract":"Large slewing bearings are characterized by low rotational speed, high load bearing and long design service life, and their operating condition determines whether the rotating machinery can operate normally. Condition monitoring and prediction of degradation trends in slewing bearings have long been hot topics of research. Traditional health indicator construction and prediction methods require human extraction of features and huge amounts of state label data. To avoid these problems, a health indicator construction method is proposed that combines densely connected fully convolutional auto-encoder (DFCAE) networks with Hidden Markov Model (HMM) in this paper. The proposed method is verified by large-scale slewing bearing data from the highly accelerated life test. The proposed methodology is also compared with other common methods of constructing health indicators, and the results prove that the proposed methodology constructs better health indicators. Finally, machine learning and deep learning networks are used to predict the degradation trend of the test slewing bearing. The prediction results show that the proposed methodology can meet the prediction requirements in the actual operation of large slewing bearings.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126404842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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