Pub Date : 2022-10-13DOI: 10.1109/PHM-Yantai55411.2022.9941943
Weixiong Jiang, Zhenqiao Zhu, W. Zhang, Limin Cheng, Zongzhen Ye, Jun Wu
Wind turbine gearbox is widely used in wind power turbine due to its excellent transmission characteristics. The quality of wind turbine gearbox has great impact on the turbine life security. With the development of monitoring technology, as a method to record the operation state of wind power turbine, time-domain and frequency-domain analysis has been mature. However, it is of great challenge for human to identify the faults, especially compound failure pattern in operating processes. At present work, a novel compound fault diagnosis method called Multi-stage extreme Gradient Boosting (MsXGB) is proposed, which can diagnose compound faults coupled with multiple individual fault simultaneously. The diagnosis results show that the test accuracy is 97%, and the train accuracy is up to 100%.
{"title":"Intelligent Fault Diagnosis of Wind Turbine Gearbox Based on Multi-stage Extreme Gradient Boosting","authors":"Weixiong Jiang, Zhenqiao Zhu, W. Zhang, Limin Cheng, Zongzhen Ye, Jun Wu","doi":"10.1109/PHM-Yantai55411.2022.9941943","DOIUrl":"https://doi.org/10.1109/PHM-Yantai55411.2022.9941943","url":null,"abstract":"Wind turbine gearbox is widely used in wind power turbine due to its excellent transmission characteristics. The quality of wind turbine gearbox has great impact on the turbine life security. With the development of monitoring technology, as a method to record the operation state of wind power turbine, time-domain and frequency-domain analysis has been mature. However, it is of great challenge for human to identify the faults, especially compound failure pattern in operating processes. At present work, a novel compound fault diagnosis method called Multi-stage extreme Gradient Boosting (MsXGB) is proposed, which can diagnose compound faults coupled with multiple individual fault simultaneously. The diagnosis results show that the test accuracy is 97%, and the train accuracy is up to 100%.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126480634","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 : 2022-10-13DOI: 10.1109/PHM-Yantai55411.2022.9941906
Yan Cong, Jianjun Wu, G. Wang, Zikuo Dai, Dan Song
Because the fault current is weak and difficult to be identified, a method for ground fault identification and key feature extraction in distribution network based on waveform analysis is proposed. By analyzing the mutation characteristics and transient characteristics, waveform analysis is used as the feature extraction method, combined with the normalization processing method, to obtain the target feature components. Identify fault persistence features, extract frequency band components, and obtain a set of pulse signals through mathematical morphological transformation. The positive impulse noise and negative impulse noise fault signals extracted are suppressed by combining the opening operation and the closing operation. After analyzing the characteristic quantity of distribution network, the characteristic parameters of fault identification are determined. The volt-ampere characteristics of linear distribution network components are analyzed, and fault line identification is realized according to the characteristic components. Analyze metallic ground fault, arc ground fault, and intermittent arc ground fault waveforms, divide characteristic areas, and complete ground fault identification. The experimental results show that the current transient component fluctuation curve of this method is consistent with the actual fluctuation curve, and the maximum identification accuracy and identification time are 0.988 and 20 s respectively, experiments show that this method has high accuracy and recognition rate.
{"title":"Ground Fault Identification and Key Feature Extraction Method for Distribution Network Based on Waveform Analysis","authors":"Yan Cong, Jianjun Wu, G. Wang, Zikuo Dai, Dan Song","doi":"10.1109/PHM-Yantai55411.2022.9941906","DOIUrl":"https://doi.org/10.1109/PHM-Yantai55411.2022.9941906","url":null,"abstract":"Because the fault current is weak and difficult to be identified, a method for ground fault identification and key feature extraction in distribution network based on waveform analysis is proposed. By analyzing the mutation characteristics and transient characteristics, waveform analysis is used as the feature extraction method, combined with the normalization processing method, to obtain the target feature components. Identify fault persistence features, extract frequency band components, and obtain a set of pulse signals through mathematical morphological transformation. The positive impulse noise and negative impulse noise fault signals extracted are suppressed by combining the opening operation and the closing operation. After analyzing the characteristic quantity of distribution network, the characteristic parameters of fault identification are determined. The volt-ampere characteristics of linear distribution network components are analyzed, and fault line identification is realized according to the characteristic components. Analyze metallic ground fault, arc ground fault, and intermittent arc ground fault waveforms, divide characteristic areas, and complete ground fault identification. The experimental results show that the current transient component fluctuation curve of this method is consistent with the actual fluctuation curve, and the maximum identification accuracy and identification time are 0.988 and 20 s respectively, experiments show that this method has high accuracy and recognition rate.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131461809","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 : 2022-10-13DOI: 10.1109/phm-yantai55411.2022.9941897
{"title":"PHM-Yantai 2022 Cover Page","authors":"","doi":"10.1109/phm-yantai55411.2022.9941897","DOIUrl":"https://doi.org/10.1109/phm-yantai55411.2022.9941897","url":null,"abstract":"","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130427119","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 : 2022-10-13DOI: 10.1109/PHM-Yantai55411.2022.9941968
Lei Shen, Wen Tian
In order to control the risk of direct investment in SCO (Shanghai Cooperation Organization) countries, a risk assessment method for direct investment in SCO countries based on particle swarm algorithm is proposed. The Shanghai Cooperation Organization (SCO) is an organization devoted to solving various security problems in the Eurasian region and promoting trade development and cultural exchanges among the six countries. Analyze the investment structure of SCO member states according to their national and industrial structures. The weight value of each index of risk assessment is obtained by using the judgment matrix. Combined with the consistency test, the weight of the risk assessment indicators of SCO countries' direct investment is calculated. Particle swarm optimization is used to search the optimal solution of the risk evaluation index of direct investment. According to the corresponding relationship between investment composition and main risk factors, the risk evaluation system of SCO countries' direct investment is constructed. Through the risk evaluation algorithm, the risk evaluation of SCO countries' direct investment is realized.
{"title":"Risk Assessment Method of Direct Investment in SCO Countries Based on Particle Swarm Algorithm","authors":"Lei Shen, Wen Tian","doi":"10.1109/PHM-Yantai55411.2022.9941968","DOIUrl":"https://doi.org/10.1109/PHM-Yantai55411.2022.9941968","url":null,"abstract":"In order to control the risk of direct investment in SCO (Shanghai Cooperation Organization) countries, a risk assessment method for direct investment in SCO countries based on particle swarm algorithm is proposed. The Shanghai Cooperation Organization (SCO) is an organization devoted to solving various security problems in the Eurasian region and promoting trade development and cultural exchanges among the six countries. Analyze the investment structure of SCO member states according to their national and industrial structures. The weight value of each index of risk assessment is obtained by using the judgment matrix. Combined with the consistency test, the weight of the risk assessment indicators of SCO countries' direct investment is calculated. Particle swarm optimization is used to search the optimal solution of the risk evaluation index of direct investment. According to the corresponding relationship between investment composition and main risk factors, the risk evaluation system of SCO countries' direct investment is constructed. Through the risk evaluation algorithm, the risk evaluation of SCO countries' direct investment is realized.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130470804","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 : 2022-10-13DOI: 10.1109/PHM-Yantai55411.2022.9942209
Jinhua Chen, L. Wang, Yan Huang, Yadong Li, Dawei Dong
The method of resonance sparse decomposition (RSSD) is extensively used in rolling bearing fault diagnosis. The selection of the decomposition parameters plays a decisive role in fault separation. It is difficult to accurately diagnose the weak fault of rolling bearing by traditional methods. In this paper, the fault diagnosis method of the rolling bearing is performed based on signal resonance sparse decomposition. The resonance sparse decomposition is carried out according to the different quality factors (QF) of the harmonic component and the periodic impact component in the rolling bearing fault vibration signal. The decomposition effect of the signal resonance sparse decomposition method is closely related to the quality factor. However, the quality factor selection based on human experience is often not effective, and the interpretability is not strong. To ensure the accuracy of the parameter selection, this paper proposes a multi-parameter optimization method based on the Grey-Wolf optimization algorithm (GWO) for adaptive resonance sparse decomposition. The simulation test and application example show that this method can effectively extract the fault characteristic components of the bearing, eliminate the signal interference and noise, and correctly identify the fault state of the rolling bearing.
{"title":"Fault Diagnosis of Rolling Bearing based on Optimal Resonance Sparse Decomposition","authors":"Jinhua Chen, L. Wang, Yan Huang, Yadong Li, Dawei Dong","doi":"10.1109/PHM-Yantai55411.2022.9942209","DOIUrl":"https://doi.org/10.1109/PHM-Yantai55411.2022.9942209","url":null,"abstract":"The method of resonance sparse decomposition (RSSD) is extensively used in rolling bearing fault diagnosis. The selection of the decomposition parameters plays a decisive role in fault separation. It is difficult to accurately diagnose the weak fault of rolling bearing by traditional methods. In this paper, the fault diagnosis method of the rolling bearing is performed based on signal resonance sparse decomposition. The resonance sparse decomposition is carried out according to the different quality factors (QF) of the harmonic component and the periodic impact component in the rolling bearing fault vibration signal. The decomposition effect of the signal resonance sparse decomposition method is closely related to the quality factor. However, the quality factor selection based on human experience is often not effective, and the interpretability is not strong. To ensure the accuracy of the parameter selection, this paper proposes a multi-parameter optimization method based on the Grey-Wolf optimization algorithm (GWO) for adaptive resonance sparse decomposition. The simulation test and application example show that this method can effectively extract the fault characteristic components of the bearing, eliminate the signal interference and noise, and correctly identify the fault state of the rolling bearing.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"47 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134390136","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 : 2022-10-13DOI: 10.1109/PHM-Yantai55411.2022.9941899
Wen Tian, Lei Shen
In view of the poor clustering accuracy of current hybrid large data fast clustering algorithms, a hybrid large data fast clustering algorithm considering global distribution information is proposed. Rough set algorithm is used to collect mixed data samples considering global distribution information of samples. The original mixed data entropy is calculated to complete the initial data partition. MapReduce is combined with the classical spectral clustering algorithm to complete the hybrid large data clustering analysis. So far, the hybrid big data clustering algorithm considering global distribution information of samples is designed. The experimental findings demonstrate that this method's clustering accuracy is comparatively high and that excellent clustering outcomes may be attained.
{"title":"A Fast Clustering Algorithm for Hybrid Big Data Considering the Global Distribution Information of Samples","authors":"Wen Tian, Lei Shen","doi":"10.1109/PHM-Yantai55411.2022.9941899","DOIUrl":"https://doi.org/10.1109/PHM-Yantai55411.2022.9941899","url":null,"abstract":"In view of the poor clustering accuracy of current hybrid large data fast clustering algorithms, a hybrid large data fast clustering algorithm considering global distribution information is proposed. Rough set algorithm is used to collect mixed data samples considering global distribution information of samples. The original mixed data entropy is calculated to complete the initial data partition. MapReduce is combined with the classical spectral clustering algorithm to complete the hybrid large data clustering analysis. So far, the hybrid big data clustering algorithm considering global distribution information of samples is designed. The experimental findings demonstrate that this method's clustering accuracy is comparatively high and that excellent clustering outcomes may be attained.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131541585","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 : 2022-10-13DOI: 10.1109/PHM-Yantai55411.2022.9941988
Juanzhang Xie, Fangyi Wan, Yajie Han, Xue Wang, C. Jiang, W. Cui
The landing gear cabin door locking mechanism is a key component of the aircraft, and its reliability has a direct impact on the aircraft landing gear retraction function and stealth function. Most of the thresholds for the failure characteristics of the locking mechanism are set, and for multiple failure characteristics in the multivariate degradation model, the time when different thresholds take effect may exist before and after in the regression sense, and the failure thresholds based on the correlation of multiple performance characteristics should be synchronized in the regression sense to ensure the time when they take effect. The problem of inter-threshold synchronization has not been noticed by researchers and needs attention. In the article, based on the analysis of the influencing factors of the life of the locking mechanism, the locking angle of the lock hook and the offset of the locking displacement of the lock hook are selected as the performance degradation characteristic quantities; based on the case that the failure thresholds corresponding to the two failure characteristic quantities have been determined, the degradation model is established by Wiener process, so as to obtain the failure probability density function of both; the correlation of the two failure characteristic quantities and the synchronization of their threshold combinations are discussed, and the use of Copula function is used to establish a multi-performance degradation reliability assessment model, and the synchronization of failure thresholds is verified. It is proposed that there is a need to develop a method for determining the threshold combination reflecting the correlation of the characteristic quantities.
{"title":"Failure Threshold Analysis Based On Multiple Performance Degradation Reliability Assessment Methods","authors":"Juanzhang Xie, Fangyi Wan, Yajie Han, Xue Wang, C. Jiang, W. Cui","doi":"10.1109/PHM-Yantai55411.2022.9941988","DOIUrl":"https://doi.org/10.1109/PHM-Yantai55411.2022.9941988","url":null,"abstract":"The landing gear cabin door locking mechanism is a key component of the aircraft, and its reliability has a direct impact on the aircraft landing gear retraction function and stealth function. Most of the thresholds for the failure characteristics of the locking mechanism are set, and for multiple failure characteristics in the multivariate degradation model, the time when different thresholds take effect may exist before and after in the regression sense, and the failure thresholds based on the correlation of multiple performance characteristics should be synchronized in the regression sense to ensure the time when they take effect. The problem of inter-threshold synchronization has not been noticed by researchers and needs attention. In the article, based on the analysis of the influencing factors of the life of the locking mechanism, the locking angle of the lock hook and the offset of the locking displacement of the lock hook are selected as the performance degradation characteristic quantities; based on the case that the failure thresholds corresponding to the two failure characteristic quantities have been determined, the degradation model is established by Wiener process, so as to obtain the failure probability density function of both; the correlation of the two failure characteristic quantities and the synchronization of their threshold combinations are discussed, and the use of Copula function is used to establish a multi-performance degradation reliability assessment model, and the synchronization of failure thresholds is verified. It is proposed that there is a need to develop a method for determining the threshold combination reflecting the correlation of the characteristic quantities.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130876001","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 : 2022-10-13DOI: 10.1109/PHM-Yantai55411.2022.9941929
Yi Huang, Weihua Yang
VR technology refers to virtual reality technology, which can simulate all kinds of things in the real environment, make users integrate into the simulation world and personally experience the change laws and specific characteristics of things [1]. At present, the application of virtual reality technology in training has become a major development trend of physical education. This model can help students improve the effect of physical education learning and further deepen their thinking mode and understanding of knowledge [2]. Combined with virtual reality technology and computer technology, this paper establishes a key action correction method of basketball training based on virtual reality technology. This system is composed of three-dimensional simulation database, capture motion virtual simulation model, motion technology simulation and other parts. The three-dimensional simulation of basketball based on virtual reality technology focuses on the principle, implementation method and specific application of virtual reality technology in the three-dimensional simulation of basketball. Using this method can bring more superior learning environment for athletes.
{"title":"Correction Method of Key Movements in Basketball Training Based on Virtual Reality Technology","authors":"Yi Huang, Weihua Yang","doi":"10.1109/PHM-Yantai55411.2022.9941929","DOIUrl":"https://doi.org/10.1109/PHM-Yantai55411.2022.9941929","url":null,"abstract":"VR technology refers to virtual reality technology, which can simulate all kinds of things in the real environment, make users integrate into the simulation world and personally experience the change laws and specific characteristics of things [1]. At present, the application of virtual reality technology in training has become a major development trend of physical education. This model can help students improve the effect of physical education learning and further deepen their thinking mode and understanding of knowledge [2]. Combined with virtual reality technology and computer technology, this paper establishes a key action correction method of basketball training based on virtual reality technology. This system is composed of three-dimensional simulation database, capture motion virtual simulation model, motion technology simulation and other parts. The three-dimensional simulation of basketball based on virtual reality technology focuses on the principle, implementation method and specific application of virtual reality technology in the three-dimensional simulation of basketball. Using this method can bring more superior learning environment for athletes.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131022821","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 : 2022-10-13DOI: 10.1109/PHM-Yantai55411.2022.9942124
Dongdong Wang, Deshuai Song, Gang Tang, Qingfeng Wang, Wenwu Chen
Long-term condition monitoring of rotating machinery at high sampling rate generates large amounts of operational data, causing serious problems for data storage, transmission and diagnosis. And traditional deep learning-based fault diagnosis algorithms lack a mechanism to distinguish the importance of big data features. To solve the above problems, inspired by compressed sensing (CS) and attention mechanisms, this paper proposes a fault diagnosis method for rotating machinery based on compressed sensing and deep convolutional neural networks (DCNN) with squeeze-and-excitation (SE) block, called CS-SEDCNN. Compressed sensing is used to reduce the amount of data and improve diagnostic efficiency. The SEDCNN model is constructed for fault identification. The SE block can selectively focus on important features and suppress less useful features, enhancing the feature learning ability on compressed data. The proposed method achieves high diagnostic accuracy and faster diagnostic speed on the acoustic emission dataset of the wind power condition monitoring and diagnosis test rig.
{"title":"A Fault Diagnosis Method for Rotating Machinery Based on Compressed Sensing and Deep Convolutional Neural Network with SE Block","authors":"Dongdong Wang, Deshuai Song, Gang Tang, Qingfeng Wang, Wenwu Chen","doi":"10.1109/PHM-Yantai55411.2022.9942124","DOIUrl":"https://doi.org/10.1109/PHM-Yantai55411.2022.9942124","url":null,"abstract":"Long-term condition monitoring of rotating machinery at high sampling rate generates large amounts of operational data, causing serious problems for data storage, transmission and diagnosis. And traditional deep learning-based fault diagnosis algorithms lack a mechanism to distinguish the importance of big data features. To solve the above problems, inspired by compressed sensing (CS) and attention mechanisms, this paper proposes a fault diagnosis method for rotating machinery based on compressed sensing and deep convolutional neural networks (DCNN) with squeeze-and-excitation (SE) block, called CS-SEDCNN. Compressed sensing is used to reduce the amount of data and improve diagnostic efficiency. The SEDCNN model is constructed for fault identification. The SE block can selectively focus on important features and suppress less useful features, enhancing the feature learning ability on compressed data. The proposed method achieves high diagnostic accuracy and faster diagnostic speed on the acoustic emission dataset of the wind power condition monitoring and diagnosis test rig.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132937914","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 : 2022-10-13DOI: 10.1109/phm-yantai55411.2022.9941748
Yazhou Li, W. Dai, Tong Li
The existing machining process condition monitoring methods usually only monitor the single anomaly, ignoring the multi-factor coupling anomaly in the actual complex machining process. Aiming at three kinds of typical anomalies frequently occurring in cutting, a new multi-factor coupling machining condition monitoring method based on ordinal pattern (OP) analysis and image matching is proposed. Firstly, the OP analysis model is developed to transform the condition monitoring signal into a gray image based on multi-parameter ordinal pattern spectrum (OPS), which optimizes the parameter selection process. Then, an OPS image dictionary template set of different condition monitoring signals is established. A condition recognition method based on OPS image matching is proposed to identify the sample processing state. Finally, a cutting experiment with 8 machining states is designed to verify the effectiveness of the method. The results show that the proposed method can accurately identify various cutting anomalies in different machining environments.
{"title":"Multi-factor machining condition monitoring method based on ordinal pattern analysis and image matching","authors":"Yazhou Li, W. Dai, Tong Li","doi":"10.1109/phm-yantai55411.2022.9941748","DOIUrl":"https://doi.org/10.1109/phm-yantai55411.2022.9941748","url":null,"abstract":"The existing machining process condition monitoring methods usually only monitor the single anomaly, ignoring the multi-factor coupling anomaly in the actual complex machining process. Aiming at three kinds of typical anomalies frequently occurring in cutting, a new multi-factor coupling machining condition monitoring method based on ordinal pattern (OP) analysis and image matching is proposed. Firstly, the OP analysis model is developed to transform the condition monitoring signal into a gray image based on multi-parameter ordinal pattern spectrum (OPS), which optimizes the parameter selection process. Then, an OPS image dictionary template set of different condition monitoring signals is established. A condition recognition method based on OPS image matching is proposed to identify the sample processing state. Finally, a cutting experiment with 8 machining states is designed to verify the effectiveness of the method. The results show that the proposed method can accurately identify various cutting anomalies in different machining environments.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133585131","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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