Pub Date : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8942988
Rui Wang, Zhisheng Zhang, Zhijie Xia, J. Miao, Yiming Guo
The fault diagnosis of CNC machine tools has become an important area of Prognostic and Health Management (PHM). The failure of rolling bearings on spindle is main cause of machine tool faults. Therefore, the significant focus of health management of CNC machine tools and other rotating machines is fault diagnosis of rolling bearings. In terms of the fault diagnosis, it is the most critical task to extracting bearing fault characteristics from vibration signals of rolling bearings. As a result, a new fault diagnosis method for bearing fault classification is proposed in this paper, which is built on the hierarchical entropy and improved Cuckoo Search-Support Vector Machine(CS-SVM). Firstly, ensemble empirical mode decomposition(EEMD) is adopted to decompose time domain vibration signals, aiming at eliminating modal confusion in empirical mode decomposition(EMD) method. Afterwards, the hierarchical entropy is chosen as fault feature parameters compared with sample entropy to construct feature vectors. In addition, the classification algorithm of multiple SVM optimized by the improved CS algorithm is utilized to identify rolling bearing fault modes. Finally, the proposed method is verified through the data taken from the Case Western Reserve University (CWRU) Bearing Data Center. The result demonstrates that the proposed method has promising performance and achieves accurate fault classification accuracy in rolling bearing fault diagnosis in comparison with other methods.
{"title":"A new approach for rolling bearing fault diagnosis based on EEMD hierarchical entropy and improved CS-SVM","authors":"Rui Wang, Zhisheng Zhang, Zhijie Xia, J. Miao, Yiming Guo","doi":"10.1109/phm-qingdao46334.2019.8942988","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942988","url":null,"abstract":"The fault diagnosis of CNC machine tools has become an important area of Prognostic and Health Management (PHM). The failure of rolling bearings on spindle is main cause of machine tool faults. Therefore, the significant focus of health management of CNC machine tools and other rotating machines is fault diagnosis of rolling bearings. In terms of the fault diagnosis, it is the most critical task to extracting bearing fault characteristics from vibration signals of rolling bearings. As a result, a new fault diagnosis method for bearing fault classification is proposed in this paper, which is built on the hierarchical entropy and improved Cuckoo Search-Support Vector Machine(CS-SVM). Firstly, ensemble empirical mode decomposition(EEMD) is adopted to decompose time domain vibration signals, aiming at eliminating modal confusion in empirical mode decomposition(EMD) method. Afterwards, the hierarchical entropy is chosen as fault feature parameters compared with sample entropy to construct feature vectors. In addition, the classification algorithm of multiple SVM optimized by the improved CS algorithm is utilized to identify rolling bearing fault modes. Finally, the proposed method is verified through the data taken from the Case Western Reserve University (CWRU) Bearing Data Center. The result demonstrates that the proposed method has promising performance and achieves accurate fault classification accuracy in rolling bearing fault diagnosis in comparison with other methods.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116227254","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 : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8942872
M. Zan, Wang Peng, L. Ruihua, Huang Jianbo
The failure detection of the GNSS airborne system can reduce the navigation and positioning failure rate of the GNSS airborne system. While, it takes more longer time to complete the failure detection by traditional failure detection model. Therefore, a novel failure detection model of the GNSS airborne system has been considered and developed by differential equation of gray theory to predict the next arrival time of the heartbeat message when GNSS fails. Furthermore, the reliable message communication can be realized through the prediction result, and failure judgment of the GNSS airborne system, which is defined and utilized as the preliminary judgment basis, can be carried out. Then, the failure detection model of the GNSS airborne system is established in basis on combination logic between rumor heartbeat realization mode and monitoring heartbeat realization mode. Finally the proposed model in this present paper had been simulated and proved the shortest response time, which proves the performance of the model.
{"title":"A Quick-response Failure Detection Model of GNSS Airborne System","authors":"M. Zan, Wang Peng, L. Ruihua, Huang Jianbo","doi":"10.1109/phm-qingdao46334.2019.8942872","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942872","url":null,"abstract":"The failure detection of the GNSS airborne system can reduce the navigation and positioning failure rate of the GNSS airborne system. While, it takes more longer time to complete the failure detection by traditional failure detection model. Therefore, a novel failure detection model of the GNSS airborne system has been considered and developed by differential equation of gray theory to predict the next arrival time of the heartbeat message when GNSS fails. Furthermore, the reliable message communication can be realized through the prediction result, and failure judgment of the GNSS airborne system, which is defined and utilized as the preliminary judgment basis, can be carried out. Then, the failure detection model of the GNSS airborne system is established in basis on combination logic between rumor heartbeat realization mode and monitoring heartbeat realization mode. Finally the proposed model in this present paper had been simulated and proved the shortest response time, which proves the performance of the model.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116461968","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 : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8943034
Wanglong Zhan, Du Siyuan, Yue Guo-dong, Z. Huimin
The spindle system will vibrate due to mass unbalance during high speed operation, which will affect the machining accuracy. In order to compensate the dynamic balance quality of unbalanced vibration, spindle dynamic balance processing can be carried out. In the process of dynamic balance processing, the extraction of unbalanced vibration signal is the key to affect the balance quality. The vibration signal extraction experiment of high-speed spindle was carried out by using all-phase FFT method. The vibration signal extracted by the all-phase FFT method is taken as the input of the influence coefficient method, and the amplitude of vibration is obviously reduced, and the balance precision reaches 65.21%. Compared with the cross-correlation method, the unbalanced vibration is effectively suppressed. The results show that the all-phase FFT method has the characteristics of stability and high balance precision, and can be applied to the vibration signal extraction of high-speed spindle and the vibration signal extraction of rotary subclass.
{"title":"Experimental Study on Unbalanced Vibration Signal of High Speed Spindle Based on All Phase Fast Fourier Transform","authors":"Wanglong Zhan, Du Siyuan, Yue Guo-dong, Z. Huimin","doi":"10.1109/phm-qingdao46334.2019.8943034","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8943034","url":null,"abstract":"The spindle system will vibrate due to mass unbalance during high speed operation, which will affect the machining accuracy. In order to compensate the dynamic balance quality of unbalanced vibration, spindle dynamic balance processing can be carried out. In the process of dynamic balance processing, the extraction of unbalanced vibration signal is the key to affect the balance quality. The vibration signal extraction experiment of high-speed spindle was carried out by using all-phase FFT method. The vibration signal extracted by the all-phase FFT method is taken as the input of the influence coefficient method, and the amplitude of vibration is obviously reduced, and the balance precision reaches 65.21%. Compared with the cross-correlation method, the unbalanced vibration is effectively suppressed. The results show that the all-phase FFT method has the characteristics of stability and high balance precision, and can be applied to the vibration signal extraction of high-speed spindle and the vibration signal extraction of rotary subclass.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116644863","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 : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8942951
Mengling Wu, Gang Liu, Jinjun Lu, Xiaofeng Geng
Speed sensors installed on the axes of high-speed train will lead to faults due to the vibration and electromagnetic interference during train operation. At present the braking system can't detect all faults of speed sensor but misdirect the axle lock fault, which affects the safety of train operation. Therefore, this paper proposes an integral intelligent fault diagnosis method for speed sensor of high-speed train brake system, which realizes real-time detection of speed sensor anomalies and accurate location of the axis of the speed sensor fault. Firstly, the traditional principal component analysis method is improved by proposing a comprehensive monitoring statistic to realize real-time fault detection of speed sensor. Then, the modified reconstruction based contribution plot based on the idea of combination maximization is adopted to achieve accurate fault location of speed sensor. In addition, the fault injection experiments are conducted, the results prove the method can diagnose the fault of speed sensor accurately and effectively, and solve the hidden trouble of high-speed train operation.
{"title":"Research on Fault Diagnosis Method for Speed Sensor of High-Speed Train","authors":"Mengling Wu, Gang Liu, Jinjun Lu, Xiaofeng Geng","doi":"10.1109/phm-qingdao46334.2019.8942951","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942951","url":null,"abstract":"Speed sensors installed on the axes of high-speed train will lead to faults due to the vibration and electromagnetic interference during train operation. At present the braking system can't detect all faults of speed sensor but misdirect the axle lock fault, which affects the safety of train operation. Therefore, this paper proposes an integral intelligent fault diagnosis method for speed sensor of high-speed train brake system, which realizes real-time detection of speed sensor anomalies and accurate location of the axis of the speed sensor fault. Firstly, the traditional principal component analysis method is improved by proposing a comprehensive monitoring statistic to realize real-time fault detection of speed sensor. Then, the modified reconstruction based contribution plot based on the idea of combination maximization is adopted to achieve accurate fault location of speed sensor. In addition, the fault injection experiments are conducted, the results prove the method can diagnose the fault of speed sensor accurately and effectively, and solve the hidden trouble of high-speed train operation.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121223354","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 : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8942942
B. Yan, Yifan Zhou, D. Yu, Xianpeng Wang
With the further opening of the bidding market in China, the accuracy of electricity price prediction directly affects the operational decisions and profits of power producers. The core factor that affects electricity price is power flow. In the early stage of electric power reform, the data of electricity price was too insufficient to support the forecasting analysis. This paper assists electric power traders to fill in the appropriate amount of electricity during the transaction process by predicting the relevant cross-section power flow. Computational methods are complex and require data of many variables at present. Therefore, this paper uses autoregressive integrated moving average (ARIMA) model and long short-term memory (LSTM) model to predict the power flow. The prediction error of the model is less than 5%. Furthermore, the conclusion shows that there is no difference between weekdays and weekends, and the power flow is a stationary time series. Based on the result of this research, some decision-making suggestions that can maximize the profit of the manufacturer are given.
{"title":"Power Flow Prediction: A Case in Ningxia Electricity Market","authors":"B. Yan, Yifan Zhou, D. Yu, Xianpeng Wang","doi":"10.1109/phm-qingdao46334.2019.8942942","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942942","url":null,"abstract":"With the further opening of the bidding market in China, the accuracy of electricity price prediction directly affects the operational decisions and profits of power producers. The core factor that affects electricity price is power flow. In the early stage of electric power reform, the data of electricity price was too insufficient to support the forecasting analysis. This paper assists electric power traders to fill in the appropriate amount of electricity during the transaction process by predicting the relevant cross-section power flow. Computational methods are complex and require data of many variables at present. Therefore, this paper uses autoregressive integrated moving average (ARIMA) model and long short-term memory (LSTM) model to predict the power flow. The prediction error of the model is less than 5%. Furthermore, the conclusion shows that there is no difference between weekdays and weekends, and the power flow is a stationary time series. Based on the result of this research, some decision-making suggestions that can maximize the profit of the manufacturer are given.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125330864","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 : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8943015
Shukai Guan, B. Wan, Zhongqing Zhang, J. Zuo
In the development and production stages of components, the reliability enhancement test (RET) has been used as one of the necessary test methods to identify weak links in product design and production. Due to the diversity and the complex environment of components, how to reduce the cost of RET and stimulate the potential defects of the device products quickly has become the primary research goal. In this paper, a design method of component multi-stress RET based on fuzzy theory is presented. First, we use the FMECA to obtain the sensitive stresses of components. The sensitive stresses order is measured by the fuzzy theory. Second, we use the double-crossed stepwise stress method to verify the sensitive stresses sequence. Third, the stress combination of RET is determined by using the fuzzy matrix calculation results and the data distribution characteristics. Fourth, using the failure physics theory and orthogonal experiment methods to optimize the design of RET. Finally, a case study with A/D converter is carried out to verify the above methods. The optimization method of multi-stress RET is helpful to quantify different factors and quickly excite potential defects of components by using failure physical simulations.
{"title":"The Optimization Method of Component Multi-stress Reliability Enhancement Test Based on Fuzzy Theory","authors":"Shukai Guan, B. Wan, Zhongqing Zhang, J. Zuo","doi":"10.1109/phm-qingdao46334.2019.8943015","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8943015","url":null,"abstract":"In the development and production stages of components, the reliability enhancement test (RET) has been used as one of the necessary test methods to identify weak links in product design and production. Due to the diversity and the complex environment of components, how to reduce the cost of RET and stimulate the potential defects of the device products quickly has become the primary research goal. In this paper, a design method of component multi-stress RET based on fuzzy theory is presented. First, we use the FMECA to obtain the sensitive stresses of components. The sensitive stresses order is measured by the fuzzy theory. Second, we use the double-crossed stepwise stress method to verify the sensitive stresses sequence. Third, the stress combination of RET is determined by using the fuzzy matrix calculation results and the data distribution characteristics. Fourth, using the failure physics theory and orthogonal experiment methods to optimize the design of RET. Finally, a case study with A/D converter is carried out to verify the above methods. The optimization method of multi-stress RET is helpful to quantify different factors and quickly excite potential defects of components by using failure physical simulations.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"269 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123482690","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 : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8942896
Wen Jia, Luo Haimin, W. Xiao
a hierarchical PHM (Prognostic and Health Management) architecture divided into subsystem-level and system-level is proposed with its functions and interfaces at various levels to satisfy PHM requirements of the integrated modular mission system. At the subsystem level, integrated condition monitoring method is developed to monitor the operational conditions of various modules, data buses and functional applications according to their characteristics and requirements. At the system level, a MBR (Model-based Reasoning) engine and its diagnostic knowledge model are developed for the integrated PHM data processing, and a graphical PHM display-control interface and a PHM database are designed to display and store PHM data centrally. The overall design method is applied on a project of the scout’s integrated modular mission system and a PHM subsystem is developed, which can provide integrated health condition monitoring and accurate fault diagnosis for the mission system, as well as the real-time and comprehensive health information for pilot and maintenance personnel.
{"title":"Application and Design of PHM in Aircraft’s Integrated Modular Mission System","authors":"Wen Jia, Luo Haimin, W. Xiao","doi":"10.1109/phm-qingdao46334.2019.8942896","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942896","url":null,"abstract":"a hierarchical PHM (Prognostic and Health Management) architecture divided into subsystem-level and system-level is proposed with its functions and interfaces at various levels to satisfy PHM requirements of the integrated modular mission system. At the subsystem level, integrated condition monitoring method is developed to monitor the operational conditions of various modules, data buses and functional applications according to their characteristics and requirements. At the system level, a MBR (Model-based Reasoning) engine and its diagnostic knowledge model are developed for the integrated PHM data processing, and a graphical PHM display-control interface and a PHM database are designed to display and store PHM data centrally. The overall design method is applied on a project of the scout’s integrated modular mission system and a PHM subsystem is developed, which can provide integrated health condition monitoring and accurate fault diagnosis for the mission system, as well as the real-time and comprehensive health information for pilot and maintenance personnel.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122377392","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 : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8942970
Hang Yao, X. Jia, Bo Wang, B. Guo
Lithium-ion battery is the main energy source widely used in many fields. Therefore, it is particularly essential for estimating the health of lithium-ion battery accurately, especially in important fields such as aerospace, rail transit and satellite. For lithium-ion battery, the battery capacity is a health index (HI) that best reflects its performance degradation. By estimating the battery capacity, the health status of the lithium-ion battery can be clearly identified. However, there are technical barriers to the direct measurement of battery capacity in engineering, and many characteristics and capacities of lithium-ion batteries have abrupt changes, so that it is difficult to calculate the battery capacity accurately by formula calculation. In this paper, a new method of genetic programming combined model is proposed, which can calculate the capacity of lithium-ion battery by formulating multiple monitored features with a certain precision. Therefore, the functional relationship between multiple features and HI is well measured, which lays a good foundation for the subsequent life prediction of battery.
{"title":"A new method for estimating lithium-ion battery capacity using genetic programming combined model","authors":"Hang Yao, X. Jia, Bo Wang, B. Guo","doi":"10.1109/phm-qingdao46334.2019.8942970","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942970","url":null,"abstract":"Lithium-ion battery is the main energy source widely used in many fields. Therefore, it is particularly essential for estimating the health of lithium-ion battery accurately, especially in important fields such as aerospace, rail transit and satellite. For lithium-ion battery, the battery capacity is a health index (HI) that best reflects its performance degradation. By estimating the battery capacity, the health status of the lithium-ion battery can be clearly identified. However, there are technical barriers to the direct measurement of battery capacity in engineering, and many characteristics and capacities of lithium-ion batteries have abrupt changes, so that it is difficult to calculate the battery capacity accurately by formula calculation. In this paper, a new method of genetic programming combined model is proposed, which can calculate the capacity of lithium-ion battery by formulating multiple monitored features with a certain precision. Therefore, the functional relationship between multiple features and HI is well measured, which lays a good foundation for the subsequent life prediction of battery.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130610761","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 : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8942936
F. Chu, Xu Wang, Wei Zhang, Zheng-wei Yang, Yanping Cai
Diesel engine is a kind of power machinery equipment and widely used in industrial and agricultural production. Aiming at the difficulty in fault feature extraction of diesel engine, a visualized method based on the texture enhanced block non-negative matrix factorization (TE-BNMF) is proposed. The method firstly performs time-frequency analysis on the collected cylinder head vibration signals; then the local binary pattern (LBP) method is used to re-encode the vibration spectrum based on the gray distribution. After that, we use block non-negative matrix factorization algorithm (BNMF) to directly extract the feature parameters of the generated local binary feature map. By using a classifier to perform pattern recognition on the above-mentioned coding matrix, the automatic diagnosis of diesel engine faults is achieved. This method was applied to the fault diagnosis of 6 typical operating conditions of diesel engines, which can get high and stable fault recognition accuracy. The experiments show that the TE-BNMF diesel engine visualized fault diagnosis method proposed in this paper can discovery rich information contained in the spectrum image of diesel engine vibration deeply and diagnose the valve clearance fault of the diesel engine adaptively.
{"title":"Visualized Feature Extraction Method of Diesel Engine Based on Texture Enhanced Block NMF (TE-BNMF)","authors":"F. Chu, Xu Wang, Wei Zhang, Zheng-wei Yang, Yanping Cai","doi":"10.1109/phm-qingdao46334.2019.8942936","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942936","url":null,"abstract":"Diesel engine is a kind of power machinery equipment and widely used in industrial and agricultural production. Aiming at the difficulty in fault feature extraction of diesel engine, a visualized method based on the texture enhanced block non-negative matrix factorization (TE-BNMF) is proposed. The method firstly performs time-frequency analysis on the collected cylinder head vibration signals; then the local binary pattern (LBP) method is used to re-encode the vibration spectrum based on the gray distribution. After that, we use block non-negative matrix factorization algorithm (BNMF) to directly extract the feature parameters of the generated local binary feature map. By using a classifier to perform pattern recognition on the above-mentioned coding matrix, the automatic diagnosis of diesel engine faults is achieved. This method was applied to the fault diagnosis of 6 typical operating conditions of diesel engines, which can get high and stable fault recognition accuracy. The experiments show that the TE-BNMF diesel engine visualized fault diagnosis method proposed in this paper can discovery rich information contained in the spectrum image of diesel engine vibration deeply and diagnose the valve clearance fault of the diesel engine adaptively.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130476090","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 : 2019-10-01DOI: 10.1109/phm-qingdao46334.2019.8942987
Wang Yang, Dequan Yu, Taisheng Zheng, Wenbo Wu, Zhenxiang Li, Hongyong Fu
As equipment becomes more and more complex, it is increasingly difficult to manually extract and select fault features manually based on expert experience or signal processing techniques. In addition, the shallow model such as BP neural network and SVM have trouble to deal with the complex mapping relationship with respect to the measured signal and the health condition of the equipment, who faces the problem of dimensional disaster. Combined with the advantages of deep confidence network (DBN) in features extraction and deal with high-dimensional and nonlinear samples, a fault feature extraction and diagnosis method based on deep confidence network for gearbox is investigated in this framework. The method uses the original time domain signal to train the deep confidence network and completes the intelligent diagnosis through deep learning. The preponderance is that it can take out the dependence on a great quantity of signal processing techniques and diagnostic experience, and accomplish the extraction of fault features and the intelligent diagnosis of health status with the characteristic of self-adaption. The method has no periodic requirements for time domain signals, and has strong versatility and adaptability. The experimental results of the fault diagnosis for the planetary gearbox demonstrated the feasibility and superiority of the presented method.
{"title":"Fault Diagnosis For Gearbox Based On Deep Belief Network","authors":"Wang Yang, Dequan Yu, Taisheng Zheng, Wenbo Wu, Zhenxiang Li, Hongyong Fu","doi":"10.1109/phm-qingdao46334.2019.8942987","DOIUrl":"https://doi.org/10.1109/phm-qingdao46334.2019.8942987","url":null,"abstract":"As equipment becomes more and more complex, it is increasingly difficult to manually extract and select fault features manually based on expert experience or signal processing techniques. In addition, the shallow model such as BP neural network and SVM have trouble to deal with the complex mapping relationship with respect to the measured signal and the health condition of the equipment, who faces the problem of dimensional disaster. Combined with the advantages of deep confidence network (DBN) in features extraction and deal with high-dimensional and nonlinear samples, a fault feature extraction and diagnosis method based on deep confidence network for gearbox is investigated in this framework. The method uses the original time domain signal to train the deep confidence network and completes the intelligent diagnosis through deep learning. The preponderance is that it can take out the dependence on a great quantity of signal processing techniques and diagnostic experience, and accomplish the extraction of fault features and the intelligent diagnosis of health status with the characteristic of self-adaption. The method has no periodic requirements for time domain signals, and has strong versatility and adaptability. The experimental results of the fault diagnosis for the planetary gearbox demonstrated the feasibility and superiority of the presented method.","PeriodicalId":259179,"journal":{"name":"2019 Prognostics and System Health Management Conference (PHM-Qingdao)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127778312","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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