Pub Date : 2022-05-01DOI: 10.1109/PHM2022-London52454.2022.00035
Qianfeng Lin, Jooyoung Son
COVID-19 is spreading globally, and this spread is continuous. Ships have become the leading platform for virus transmission as a means of transportation. The small space of ships makes the possibility of virus outbreaks highly increased. The current way to effectively interrupt the spread of the virus is to track close contacts and physically isolate them. Therefore, the identification of close contacts becomes critical. This paper proposes a close contact identification algorithm applicable to the ship environment. The user ID is creatively proposed as the initialized location point cluster in this algorithm. And the KDE is introduced into the clustering process of the algorithm, and the center of the cluster is calculated by using the KDE of the location points as weights. The threshold value is used as the criterion for merging the clusters. Finally, the correct cluster result is obtained. This algorithm can provide technical support for ship companies to sustainably manage ships in the post-epidemic era, thus serving the purpose of maximizing the protection of ship passengers' health.
{"title":"A Clustering Mechanism to Identify Close Contact for the Ship Passenger Health","authors":"Qianfeng Lin, Jooyoung Son","doi":"10.1109/PHM2022-London52454.2022.00035","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00035","url":null,"abstract":"COVID-19 is spreading globally, and this spread is continuous. Ships have become the leading platform for virus transmission as a means of transportation. The small space of ships makes the possibility of virus outbreaks highly increased. The current way to effectively interrupt the spread of the virus is to track close contacts and physically isolate them. Therefore, the identification of close contacts becomes critical. This paper proposes a close contact identification algorithm applicable to the ship environment. The user ID is creatively proposed as the initialized location point cluster in this algorithm. And the KDE is introduced into the clustering process of the algorithm, and the center of the cluster is calculated by using the KDE of the location points as weights. The threshold value is used as the criterion for merging the clusters. Finally, the correct cluster result is obtained. This algorithm can provide technical support for ship companies to sustainably manage ships in the post-epidemic era, thus serving the purpose of maximizing the protection of ship passengers' health.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125291108","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 : 2022-05-01DOI: 10.1109/PHM2022-London52454.2022.00034
Ziyu Shi, Ping Li, Mengyi Zhao
There are hundreds of systems and components in the nuclear power plant. In order to better manage the nuclear power plant, it is necessary to intelligently manage the important components of the nuclear power plant. This paper studies the design principles of nuclear power plant health management and intelligent operation and maintenance system, and summarizes the system design process. Complete relevant work according to the system design outline. The work outline is used to guide the nuclear power plant to fully implement the health management and intelligent operation and maintenance system and deployment, so that the whole work can be followed by rules to ensure the orderly progress of the work. According to the relevant working experience of nuclear power plants at home and abroad, through the relevant analysis of the design of important equipment and platforms related to nuclear power plants, the health management and intelligent operation and maintenance system of nuclear power plants are determined. This paper puts forward the overall framework of the system design, which can provide a reference for the intelligent operation and maintenance of nuclear power plant.
{"title":"Design of health management and intelligent operation and maintenance system for nuclear power plant","authors":"Ziyu Shi, Ping Li, Mengyi Zhao","doi":"10.1109/PHM2022-London52454.2022.00034","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00034","url":null,"abstract":"There are hundreds of systems and components in the nuclear power plant. In order to better manage the nuclear power plant, it is necessary to intelligently manage the important components of the nuclear power plant. This paper studies the design principles of nuclear power plant health management and intelligent operation and maintenance system, and summarizes the system design process. Complete relevant work according to the system design outline. The work outline is used to guide the nuclear power plant to fully implement the health management and intelligent operation and maintenance system and deployment, so that the whole work can be followed by rules to ensure the orderly progress of the work. According to the relevant working experience of nuclear power plants at home and abroad, through the relevant analysis of the design of important equipment and platforms related to nuclear power plants, the health management and intelligent operation and maintenance system of nuclear power plants are determined. This paper puts forward the overall framework of the system design, which can provide a reference for the intelligent operation and maintenance of nuclear power plant.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128351816","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 : 2022-05-01DOI: 10.1109/PHM2022-London52454.2022.00075
He Li, Feng Ji, Kang Dai
As a key component of rotating machinery, bearing plays an irreplaceable role in the operation of rotating machinery. The ability to identify bearing faults effectively and timely can ensure the safe operation of the equipment. In this paper, a logic diagnosis method framework of bearing fault feature pattern recognition was proposed by using ACGAN model structure. With the same excellent learning efficiency, the multi-layer convolution layer structure was used to ensure the learning ability of the network. Finally, a series of experiments were conducted. Experiments’ results indicated that compared with a single CNN, the improved ACGAN network architecture had better learning ability and fault state recognition rate.
{"title":"A Method of Bearing Fault Feature Pattern Recognition Based on Improved ACGAN","authors":"He Li, Feng Ji, Kang Dai","doi":"10.1109/PHM2022-London52454.2022.00075","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00075","url":null,"abstract":"As a key component of rotating machinery, bearing plays an irreplaceable role in the operation of rotating machinery. The ability to identify bearing faults effectively and timely can ensure the safe operation of the equipment. In this paper, a logic diagnosis method framework of bearing fault feature pattern recognition was proposed by using ACGAN model structure. With the same excellent learning efficiency, the multi-layer convolution layer structure was used to ensure the learning ability of the network. Finally, a series of experiments were conducted. Experiments’ results indicated that compared with a single CNN, the improved ACGAN network architecture had better learning ability and fault state recognition rate.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"727 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122005029","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 : 2022-05-01DOI: 10.1109/PHM2022-London52454.2022.00045
El Yousfi Bilal, Soualhi Abdenour, Medjaher Kamal, Guillet François
Condition monitoring of gearbox elements is a crucial task for manufacturers in order to guarantee machines availability, reliability and labor safety. Thus, motor current-based maintenance presents many advantages over traditional vibration-based maintenance, as it is non-invasive, inexpensive, and widely applicable since the majority of today’s machines are driven by induction motors. Therefore, several studies have been realized recently in order to develop efficient condition monitoring programs based on motor current analysis. In this paper, a diagnostic method of gearbox faults based on motor current analysis is developed using supervised machine learning techniques. A method is proposed to remove the effect of the load level on the classification efficiency by using the sum of the phase currents instead of the single-phase currents. A dimensionality reduction flowchart based on the singular value decomposition (SVD) algorithm is proposed in this study in order to remove the operating speed effect on the diagnostic accuracy. Two robust health indicators independent of the operating speed and load are constructed and injected as inputs of varying machine-learning models in order to classify the different health states of the gearbox. The developed health indicators showed a good accuracy in diagnosing gears and bearings faults.
{"title":"A Diagnosis Scheme of Gearbox Faults Based on Machine Learning and Motor Current Analysis","authors":"El Yousfi Bilal, Soualhi Abdenour, Medjaher Kamal, Guillet François","doi":"10.1109/PHM2022-London52454.2022.00045","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00045","url":null,"abstract":"Condition monitoring of gearbox elements is a crucial task for manufacturers in order to guarantee machines availability, reliability and labor safety. Thus, motor current-based maintenance presents many advantages over traditional vibration-based maintenance, as it is non-invasive, inexpensive, and widely applicable since the majority of today’s machines are driven by induction motors. Therefore, several studies have been realized recently in order to develop efficient condition monitoring programs based on motor current analysis. In this paper, a diagnostic method of gearbox faults based on motor current analysis is developed using supervised machine learning techniques. A method is proposed to remove the effect of the load level on the classification efficiency by using the sum of the phase currents instead of the single-phase currents. A dimensionality reduction flowchart based on the singular value decomposition (SVD) algorithm is proposed in this study in order to remove the operating speed effect on the diagnostic accuracy. Two robust health indicators independent of the operating speed and load are constructed and injected as inputs of varying machine-learning models in order to classify the different health states of the gearbox. The developed health indicators showed a good accuracy in diagnosing gears and bearings faults.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122498855","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}
Wireless power transfer (WPT) technology can realize high-efficiency and high-power remote wireless power supply. In recent years, this technology has attracted more and more attention in the application of electric vehicles (EVs). However, due to the randomness of electric vehicle parking, it is difficult to ensure that the transmitter and receiver of WPT system do not deviate. The offset between transmitter and receiver will reduce the coupling coefficient of the system, so as to reduce the transmission efficiency of the system. Therefore, in the static wireless charging technology of electric vehicles, efficient and stable power and efficient transmission are one of the most important factors to be considered. Therefore, an anti-offset optimization design scheme of electric vehicle wireless charging coupling mechanism based on genetic algorithm is proposed in this paper. Firstly, the circuit model of double coil magnetic coupling WPT system is established, and the efficiency characteristics of electric vehicle wireless charging system are analyzed. Secondly, the magnetic field distribution of circular coil and square coil is compared. Based on genetic algorithm, a new non equidistant square transmitting coil is designed to realize the uniform distribution of magnetic field at a specific charging height. In addition, the receiving coil is optimized according to the spatial magnetic field distribution generated by the transmitting coil, and a three-dimensional helical receiving coil with unequal radius is proposed. The experimental results show that the electric vehicle wireless charging system has good anti offset ability and high efficiency output.
{"title":"Optimization Design of Anti-offset Coupling Mechanism for WPT Systems Based on Genetic Algorithm","authors":"Qingsheng Yang, Chao Jiang, Xinping Wang, Chunpeng Li, Guofei Guan, Qiqi Luan","doi":"10.1109/PHM2022-London52454.2022.00085","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00085","url":null,"abstract":"Wireless power transfer (WPT) technology can realize high-efficiency and high-power remote wireless power supply. In recent years, this technology has attracted more and more attention in the application of electric vehicles (EVs). However, due to the randomness of electric vehicle parking, it is difficult to ensure that the transmitter and receiver of WPT system do not deviate. The offset between transmitter and receiver will reduce the coupling coefficient of the system, so as to reduce the transmission efficiency of the system. Therefore, in the static wireless charging technology of electric vehicles, efficient and stable power and efficient transmission are one of the most important factors to be considered. Therefore, an anti-offset optimization design scheme of electric vehicle wireless charging coupling mechanism based on genetic algorithm is proposed in this paper. Firstly, the circuit model of double coil magnetic coupling WPT system is established, and the efficiency characteristics of electric vehicle wireless charging system are analyzed. Secondly, the magnetic field distribution of circular coil and square coil is compared. Based on genetic algorithm, a new non equidistant square transmitting coil is designed to realize the uniform distribution of magnetic field at a specific charging height. In addition, the receiving coil is optimized according to the spatial magnetic field distribution generated by the transmitting coil, and a three-dimensional helical receiving coil with unequal radius is proposed. The experimental results show that the electric vehicle wireless charging system has good anti offset ability and high efficiency output.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133924937","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 : 2022-05-01DOI: 10.1109/PHM2022-London52454.2022.00079
Laihua Fang, Xunxian Shi, Sixin Song, Xiaojie Wang
Production safety is an important guarantee for the high-quality development of industrial enterprises. Factors affecting the safety of industrial enterprises are increasing, and various potential hazards are intertwined and superimposed, resulting in frequent occurrence of accidents. In order to eliminate potential hazards timely, reduce risk of accidents, a safety management and control system based on Industrial Internet of Things (IIoT) for industrial enterprises is constructed, making full use of the advantages of IIoT in rapid perception, real-time monitoring, advanced early warning, dynamic optimization, intelligent decision-making and linkage disposal. The reference architecture of IIoT and application structure of IIoT-based safety platform are given. The core technologies and applications required for the safety management and control system of industrial enterprises based on IIoT are proposed. The main functions of the IIoT-based safety platform and its realization method are studied and designed in detail. Meanwhile, aiming at network information security, the network security structure of the IIoT-based safety platform is proposed. The test application of the system shows that it enhances the safety perception, monitoring, early warning, disposal and evaluation capabilities of industrial enterprises, improves intelligence of safety management and control, and reduces risk of accidents.
{"title":"Study on IIoT-based Safety Platform of Industrial Enterprises","authors":"Laihua Fang, Xunxian Shi, Sixin Song, Xiaojie Wang","doi":"10.1109/PHM2022-London52454.2022.00079","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00079","url":null,"abstract":"Production safety is an important guarantee for the high-quality development of industrial enterprises. Factors affecting the safety of industrial enterprises are increasing, and various potential hazards are intertwined and superimposed, resulting in frequent occurrence of accidents. In order to eliminate potential hazards timely, reduce risk of accidents, a safety management and control system based on Industrial Internet of Things (IIoT) for industrial enterprises is constructed, making full use of the advantages of IIoT in rapid perception, real-time monitoring, advanced early warning, dynamic optimization, intelligent decision-making and linkage disposal. The reference architecture of IIoT and application structure of IIoT-based safety platform are given. The core technologies and applications required for the safety management and control system of industrial enterprises based on IIoT are proposed. The main functions of the IIoT-based safety platform and its realization method are studied and designed in detail. Meanwhile, aiming at network information security, the network security structure of the IIoT-based safety platform is proposed. The test application of the system shows that it enhances the safety perception, monitoring, early warning, disposal and evaluation capabilities of industrial enterprises, improves intelligence of safety management and control, and reduces risk of accidents.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"272 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115963018","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 : 2022-05-01DOI: 10.1109/PHM2022-London52454.2022.00017
Xiang-yang Xia, Xu-yun Fu, S. Zhong, Xingjie Zhou, Z. Bai
Traditional methods for gas path anomaly detection cannot fully extract remarkable shape features that can represent the gas path anomaly mode. Therefore, a feature representation method based on dual segment and entropy evaluation for aeroengine gas path anomaly detection is proposed in this paper. Taking the temporal and spatial correlations of the multivariate time series into consideration, the expression rule of the anomaly mode in the multivariate gas path parameter deviation time series is analyzed, on this basis, time series subsequence segment method is determined. To obtain the features that best fit the anomaly expression rule, a dual segment method based on piecewise optimal fitting is proposed. The entropy evaluation method is introduced to comprehensively evaluate and optimize the primary features while calculating the common shape features of subsequence, and then the remarkable shape feature matrix for anomaly detection is determined. Finally, the early warning for the gas path anomaly is realized by mining the potential anomaly mode of the gas path state using isolation forest model. The experimental results show that this method can improve the accuracy of aeroengine gas path anomaly detection.
{"title":"A Feature Representation Method Based on Dual Segment and Entropy Evaluation for Aeroengine Gas Path Anomaly Detection","authors":"Xiang-yang Xia, Xu-yun Fu, S. Zhong, Xingjie Zhou, Z. Bai","doi":"10.1109/PHM2022-London52454.2022.00017","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00017","url":null,"abstract":"Traditional methods for gas path anomaly detection cannot fully extract remarkable shape features that can represent the gas path anomaly mode. Therefore, a feature representation method based on dual segment and entropy evaluation for aeroengine gas path anomaly detection is proposed in this paper. Taking the temporal and spatial correlations of the multivariate time series into consideration, the expression rule of the anomaly mode in the multivariate gas path parameter deviation time series is analyzed, on this basis, time series subsequence segment method is determined. To obtain the features that best fit the anomaly expression rule, a dual segment method based on piecewise optimal fitting is proposed. The entropy evaluation method is introduced to comprehensively evaluate and optimize the primary features while calculating the common shape features of subsequence, and then the remarkable shape feature matrix for anomaly detection is determined. Finally, the early warning for the gas path anomaly is realized by mining the potential anomaly mode of the gas path state using isolation forest model. The experimental results show that this method can improve the accuracy of aeroengine gas path anomaly detection.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123384208","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 : 2022-05-01DOI: 10.1109/PHM2022-London52454.2022.00021
Jingting You, Jun Liang, Datong Liu
Unmanned aerial vehicles play a critical role in both military and civilian applications, and their safety and reliability have also been paid more and more attention. UAV anomaly detection can detect and eliminate potential faults in a timely and effective manner, reducing the probability of accidents. Due to the influence of the complex and changeable operating environment, data shifting problems are inevitable in time series anomaly detection. Ignoring this issue may result in a significant drop in the accuracy of anomaly detection. Therefore, a UAV sensor data anomaly detection method based on Temporal Convolution Network (TCN) model transferring is proposed in this paper. First, the TCN model is pre-trained by using a large amount of data in the source domain. Then, parameters of the model are fine-tuned on the target domain. Finally, the threshold detection method is used to determine whether there is abnormality in the UAV sensor data. This work aims to address the multiple modes of UAV and improve the data-driven adaptivity for anomaly detection. In the experiments, the flight sensor data are used to verify the performance of the proposed model. The results show that the proposed method achieves high precision, high detection rate and low false detection rate in different domains.
{"title":"An Adaptable UAV Sensor Data Anomaly Detection Method Based on TCN Model Transferring","authors":"Jingting You, Jun Liang, Datong Liu","doi":"10.1109/PHM2022-London52454.2022.00021","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00021","url":null,"abstract":"Unmanned aerial vehicles play a critical role in both military and civilian applications, and their safety and reliability have also been paid more and more attention. UAV anomaly detection can detect and eliminate potential faults in a timely and effective manner, reducing the probability of accidents. Due to the influence of the complex and changeable operating environment, data shifting problems are inevitable in time series anomaly detection. Ignoring this issue may result in a significant drop in the accuracy of anomaly detection. Therefore, a UAV sensor data anomaly detection method based on Temporal Convolution Network (TCN) model transferring is proposed in this paper. First, the TCN model is pre-trained by using a large amount of data in the source domain. Then, parameters of the model are fine-tuned on the target domain. Finally, the threshold detection method is used to determine whether there is abnormality in the UAV sensor data. This work aims to address the multiple modes of UAV and improve the data-driven adaptivity for anomaly detection. In the experiments, the flight sensor data are used to verify the performance of the proposed model. The results show that the proposed method achieves high precision, high detection rate and low false detection rate in different domains.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125095817","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 : 2022-05-01DOI: 10.1109/PHM2022-London52454.2022.00057
Shanpeng Xia, Wenqiang Wang, Shengbo Zhou
This paper introduces the composition and working principle of CAN (Controller Area Network) bus communication system and describes several typical systems in detail. On this basis, the paper explains the application of CAN bus system in fault diagnosis. The conclusion shows that the proposed fault diagnosis logic CAN be used for vehicle fault diagnosis and analysis.
{"title":"Fault Diagnosis and Analysis of Automobile CAN Bus Communication","authors":"Shanpeng Xia, Wenqiang Wang, Shengbo Zhou","doi":"10.1109/PHM2022-London52454.2022.00057","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00057","url":null,"abstract":"This paper introduces the composition and working principle of CAN (Controller Area Network) bus communication system and describes several typical systems in detail. On this basis, the paper explains the application of CAN bus system in fault diagnosis. The conclusion shows that the proposed fault diagnosis logic CAN be used for vehicle fault diagnosis and analysis.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128660578","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 : 2022-05-01DOI: 10.1109/PHM2022-London52454.2022.00105
C. Liu
Safety is an important indicator to measure the performance of ship-borne helicopters, and the state monitoring of shipborne helicopters is the main way to ensure the safety of shipborne helicopters. As the flight time of the shipborne helicopter increases, many components will gradually degrade, and corresponding threshold need to be set to ensure the normal operation the shipborne helicopter. The commonly used fixed threshold method will cause false alarms due to different flight states, so it is necessary to dynamically construct the state thresholds under different flight states. Firstly, the empirical mode decomposition is used to denoise the signal, and then the extracted monitoring features of the shipborne helicopter are classified according to the stability in multiple flight states. A fixed threshold is setted by statistical characteristics for stable features. The dynamic feature selects flight parameters that are highly correlated with feature changes as indicators, and uses principal component analysis to fuse them to construct a dynamic threshold index.The proposed method is verified by actual flight data, and the result shows that the dynamic threshold index can effectively reduce the false alarm rate.
{"title":"Construction method of multi-stage degradation threshold for shipborne helicopter based on flight parameters","authors":"C. Liu","doi":"10.1109/PHM2022-London52454.2022.00105","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00105","url":null,"abstract":"Safety is an important indicator to measure the performance of ship-borne helicopters, and the state monitoring of shipborne helicopters is the main way to ensure the safety of shipborne helicopters. As the flight time of the shipborne helicopter increases, many components will gradually degrade, and corresponding threshold need to be set to ensure the normal operation the shipborne helicopter. The commonly used fixed threshold method will cause false alarms due to different flight states, so it is necessary to dynamically construct the state thresholds under different flight states. Firstly, the empirical mode decomposition is used to denoise the signal, and then the extracted monitoring features of the shipborne helicopter are classified according to the stability in multiple flight states. A fixed threshold is setted by statistical characteristics for stable features. The dynamic feature selects flight parameters that are highly correlated with feature changes as indicators, and uses principal component analysis to fuse them to construct a dynamic threshold index.The proposed method is verified by actual flight data, and the result shows that the dynamic threshold index can effectively reduce the false alarm rate.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126185948","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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