Pub Date : 2021-10-15DOI: 10.1109/PHM-Nanjing52125.2021.9612762
Jingli Yang, Yue Li, Cheng Yang, Tianyu Gao
In the actual working process of the analog circuit, the probability of multiple component failures at the same time is lower than the probability of a single component failure, which makes the single fault data samples and multiple fault data samples tend to show imbalanced characteristics. However, most of the existing data-driven analog circuit diagnosis methods focus on the balance data sample set. Therefore, it is hard to satisfy the needs of fault diagnosis during the actual working of analog circuits. In response to the problems raised above, an analog circuit fault diagnosis method based on enhanced boundary equilibrium generative adversarial network (EBEGAN) is proposed. The generator of boundary equilibrium generative adversarial networks (BEGAN) uses conditional variational auto encoder (CVAE), which can enhance the generated sample quality while ensuring sample diversity. In addition, by introducing the classified loss factor into the loss function, the discriminator has the ability to distinguish the true and false and the type of samples. The experimental results indicate that this study proposes the new method in the situation of imbalanced data, the type of fault in the analog circuit can be accurately identified. compared with the existing analog circuit fault diagnosis methods.
{"title":"Fault Diagnosis Method of Analog Circuit Based on Enhanced Boundary Equilibrium Generative Adversarial Networks","authors":"Jingli Yang, Yue Li, Cheng Yang, Tianyu Gao","doi":"10.1109/PHM-Nanjing52125.2021.9612762","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612762","url":null,"abstract":"In the actual working process of the analog circuit, the probability of multiple component failures at the same time is lower than the probability of a single component failure, which makes the single fault data samples and multiple fault data samples tend to show imbalanced characteristics. However, most of the existing data-driven analog circuit diagnosis methods focus on the balance data sample set. Therefore, it is hard to satisfy the needs of fault diagnosis during the actual working of analog circuits. In response to the problems raised above, an analog circuit fault diagnosis method based on enhanced boundary equilibrium generative adversarial network (EBEGAN) is proposed. The generator of boundary equilibrium generative adversarial networks (BEGAN) uses conditional variational auto encoder (CVAE), which can enhance the generated sample quality while ensuring sample diversity. In addition, by introducing the classified loss factor into the loss function, the discriminator has the ability to distinguish the true and false and the type of samples. The experimental results indicate that this study proposes the new method in the situation of imbalanced data, the type of fault in the analog circuit can be accurately identified. compared with the existing analog circuit fault diagnosis methods.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114110547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-15DOI: 10.1109/PHM-Nanjing52125.2021.9613045
Zihao Zhang, Xianghua Huang, Tianhong Zhang
As the cutting-edge methods of Health Management (HM), deep learning is getting more and more attention. This paper reviews the application of deep learning algorithms to HM, summarizes the advantages and defects of these algorithms, and proposes three problems to be solved as well as possible solutions. Most importantly, instead of depicting different deep learning methods from different perspectives, like statistics, algebra, topology, this paper gives a universal algebra perspective towards deep learning algorithms.
{"title":"Review Of The Application Of Deep Learning In Health Management","authors":"Zihao Zhang, Xianghua Huang, Tianhong Zhang","doi":"10.1109/PHM-Nanjing52125.2021.9613045","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9613045","url":null,"abstract":"As the cutting-edge methods of Health Management (HM), deep learning is getting more and more attention. This paper reviews the application of deep learning algorithms to HM, summarizes the advantages and defects of these algorithms, and proposes three problems to be solved as well as possible solutions. Most importantly, instead of depicting different deep learning methods from different perspectives, like statistics, algebra, topology, this paper gives a universal algebra perspective towards deep learning algorithms.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125156431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-15DOI: 10.1109/PHM-Nanjing52125.2021.9612805
B. Huang, Shunong Zhang, Xuesong Yang, Yingying Liu
Generally in a method on reliability prediction for a system, there is an assumption that the process of each component failure is independent, irrelevant and not affected by other components. However, in the actual working process of a product, the life change caused by dynamic influences among the components of the product should not be negligible. In this paper, a method on reliability prediction based on fuzzy cognitive map is proposed, and a case study using system in package (SiP) is given to determine the life of each component of the SiP under the state of dynamic mutual influences.
{"title":"Research on a Reliability Prediction Method Based on Fuzzy Cognitive Map","authors":"B. Huang, Shunong Zhang, Xuesong Yang, Yingying Liu","doi":"10.1109/PHM-Nanjing52125.2021.9612805","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612805","url":null,"abstract":"Generally in a method on reliability prediction for a system, there is an assumption that the process of each component failure is independent, irrelevant and not affected by other components. However, in the actual working process of a product, the life change caused by dynamic influences among the components of the product should not be negligible. In this paper, a method on reliability prediction based on fuzzy cognitive map is proposed, and a case study using system in package (SiP) is given to determine the life of each component of the SiP under the state of dynamic mutual influences.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125102148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-15DOI: 10.1109/PHM-Nanjing52125.2021.9612946
Qiuan Chen, Yi Liu, Shengwen Hou, Feng Duan, Zhiqiang Cai
With the development of artificial intelligence technology, data-driven PHM technology has been widely used for life cycle health management of equipment. Equipment will generate a lot of data in the process of operation and production. Analyzing the data and establishing machine learning model can accurately evaluate the operation status of equipment. Increasingly, extracting knowledge from data has become an important task in organizations for performance improvements. Data is the resource for equipment health assessment, so it is of great significance to focus on the research of data quality. Based on this, the main work of this paper is as follows. (1) The data quality issues are discussed in the context of PHM. (2) The PHM framework is proposed for improving the reliability of equipment. (3) Several machine learning algorithms are introduced for state detection. (4) The proposed technology is applied to real cases, and the results are analyzed and visualized in detail.
{"title":"Data-driven Methodology for State Detection of Gearbox in PHM Context","authors":"Qiuan Chen, Yi Liu, Shengwen Hou, Feng Duan, Zhiqiang Cai","doi":"10.1109/PHM-Nanjing52125.2021.9612946","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612946","url":null,"abstract":"With the development of artificial intelligence technology, data-driven PHM technology has been widely used for life cycle health management of equipment. Equipment will generate a lot of data in the process of operation and production. Analyzing the data and establishing machine learning model can accurately evaluate the operation status of equipment. Increasingly, extracting knowledge from data has become an important task in organizations for performance improvements. Data is the resource for equipment health assessment, so it is of great significance to focus on the research of data quality. Based on this, the main work of this paper is as follows. (1) The data quality issues are discussed in the context of PHM. (2) The PHM framework is proposed for improving the reliability of equipment. (3) Several machine learning algorithms are introduced for state detection. (4) The proposed technology is applied to real cases, and the results are analyzed and visualized in detail.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131496837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-15DOI: 10.1109/PHM-Nanjing52125.2021.9612931
Chuan Li, Zhenghua Tang, Yong Tang
In the case of no shaft speed measurement, the effective acquisition of shaft speed information is the premise of speed-related fault feature extraction, so it is necessary to preprocess the vibration signal and extract the shaft speed information from the vibration signal. In this paper, a signal preprocessing method for extracting shaft velocity information from vibration signals is described in detail. Based on the theory of signal processing, this paper summarizes the method of extracting shaft velocity signal without shaft speed measurement, and verifies the effectiveness of relevant theoretical methods and shaft speed extraction accuracy based on practical application cases.
{"title":"A Signal Processing Method For Extracting Shaft Speed Information From Vibration Signal","authors":"Chuan Li, Zhenghua Tang, Yong Tang","doi":"10.1109/PHM-Nanjing52125.2021.9612931","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612931","url":null,"abstract":"In the case of no shaft speed measurement, the effective acquisition of shaft speed information is the premise of speed-related fault feature extraction, so it is necessary to preprocess the vibration signal and extract the shaft speed information from the vibration signal. In this paper, a signal preprocessing method for extracting shaft velocity information from vibration signals is described in detail. Based on the theory of signal processing, this paper summarizes the method of extracting shaft velocity signal without shaft speed measurement, and verifies the effectiveness of relevant theoretical methods and shaft speed extraction accuracy based on practical application cases.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127965231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-15DOI: 10.1109/PHM-Nanjing52125.2021.9612938
Gong Wenjun, Sun Bin, Zheng Yuanzhu
This paper analyses the failure mechanism of components in T/R module and presents a new method about the fault prognosis for T/R modules of radar systems. The function and failure pattern has been provided firstly. Then, we provide the possible failure mechanism and present the time-related formula to describe the time-to-failure model dominated by the fault character parameter. Then, a connection between the failure mechanism and failure rate is established through the coupling formulas and failure rate distributions. Given the predicted ambient temperature, the number prediction curve of failed T/R modules in the radar system is proposed. Based on the number calculation, the spare parts can be arranged in advance to decrease the unnecessary waiting time, supporting the implementation of condition-based maintenance for radar system.
{"title":"Fault Prognosis of Transmit-receive Modules for Active Phased Array radar","authors":"Gong Wenjun, Sun Bin, Zheng Yuanzhu","doi":"10.1109/PHM-Nanjing52125.2021.9612938","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612938","url":null,"abstract":"This paper analyses the failure mechanism of components in T/R module and presents a new method about the fault prognosis for T/R modules of radar systems. The function and failure pattern has been provided firstly. Then, we provide the possible failure mechanism and present the time-related formula to describe the time-to-failure model dominated by the fault character parameter. Then, a connection between the failure mechanism and failure rate is established through the coupling formulas and failure rate distributions. Given the predicted ambient temperature, the number prediction curve of failed T/R modules in the radar system is proposed. Based on the number calculation, the spare parts can be arranged in advance to decrease the unnecessary waiting time, supporting the implementation of condition-based maintenance for radar system.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128081666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-15DOI: 10.1109/PHM-Nanjing52125.2021.9612916
Junyou Shi, Yilei Hou, Yingla Wang
At present, the domestic test modeling and simulation work is mainly based on the multi-signal model. This method is not intuitive enough and lacks applications that can dynamically display the fault transfer relationship of the BIT system, the BIT operation logic, and the fault detection and isolation demonstration. To solve this problem, this paper proposes a BIT modeling and simulation method based on state-chart diagram, and develops related modeling software. First, the basic principles and simulation ideas of state-chart diagram are introduced. Then, the BIT modeling architecture based on the state-chart diagram is introduced, and the realization of the state-chart diagram of the static structure elements and the realization of the state-chart diagram of the dynamic process elements are explained in detail. Finally, a case verification was carried out. By using the self-developed BIT dynamic modeling and simulation software, the product model, fault model and BIT model of the case were constructed, and the predicted results of testability indicators were given, which proved the effectiveness of the method.
{"title":"A Dynamic Modeling and Simulation Method of Built-in Test(BIT) Based on State-chart Diagram","authors":"Junyou Shi, Yilei Hou, Yingla Wang","doi":"10.1109/PHM-Nanjing52125.2021.9612916","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612916","url":null,"abstract":"At present, the domestic test modeling and simulation work is mainly based on the multi-signal model. This method is not intuitive enough and lacks applications that can dynamically display the fault transfer relationship of the BIT system, the BIT operation logic, and the fault detection and isolation demonstration. To solve this problem, this paper proposes a BIT modeling and simulation method based on state-chart diagram, and develops related modeling software. First, the basic principles and simulation ideas of state-chart diagram are introduced. Then, the BIT modeling architecture based on the state-chart diagram is introduced, and the realization of the state-chart diagram of the static structure elements and the realization of the state-chart diagram of the dynamic process elements are explained in detail. Finally, a case verification was carried out. By using the self-developed BIT dynamic modeling and simulation software, the product model, fault model and BIT model of the case were constructed, and the predicted results of testability indicators were given, which proved the effectiveness of the method.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128095528","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}
Various failures are prone to occur in rotating machinery due to the harsh working conditions, thereby making it a vital work to perform accurate fault diagnosis to prevent performance degradation and safety hazards. The presence of multivariate variational mode decomposition (MVMD) provides a good knowledge of how to cope with multichannel data which contains more comprehensive information. In this work, an innovative diagnostic approach based on optimized MVMD is proposed for rotating machinery. Corner-stone of this method is the optimized MVMD, a new approach extracting modes successively with the proper adjustment of initial center frequencies. It achieves the mode decomposition without prior knowledge of the number of modes and initial center frequencies which affect the decomposition results greatly. Moreover, normalized frequency-to-energy ratio is employed as an index for selection of faulty modes. Analysis and comparison results of the experiment data from defective bearing indicates that the new approach shows a prominent superiority in fault identification.
{"title":"An optimized multivariate variational mode decomposition for the fault diagnosis of rotating machinery","authors":"Q. Song, Xingxing Jiang, Qian Wang, Weiguo Huang, Juanjuan Shi, Zhongkui Zhu","doi":"10.1109/PHM-Nanjing52125.2021.9612995","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612995","url":null,"abstract":"Various failures are prone to occur in rotating machinery due to the harsh working conditions, thereby making it a vital work to perform accurate fault diagnosis to prevent performance degradation and safety hazards. The presence of multivariate variational mode decomposition (MVMD) provides a good knowledge of how to cope with multichannel data which contains more comprehensive information. In this work, an innovative diagnostic approach based on optimized MVMD is proposed for rotating machinery. Corner-stone of this method is the optimized MVMD, a new approach extracting modes successively with the proper adjustment of initial center frequencies. It achieves the mode decomposition without prior knowledge of the number of modes and initial center frequencies which affect the decomposition results greatly. Moreover, normalized frequency-to-energy ratio is employed as an index for selection of faulty modes. Analysis and comparison results of the experiment data from defective bearing indicates that the new approach shows a prominent superiority in fault identification.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"86 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133251497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-15DOI: 10.1109/PHM-Nanjing52125.2021.9612978
Yang Yi, Zhang Lun, Yin Zhengyang, Wang Bozheng, Shen Guoji, Zhou Yang, Hu Niaoqing
Due to the complicated structure and harsh working environment, the marine propulsion shaft suffers from excessive vibrations in torsional, longitudinal and their coupled vibration modes. The coupled torsional-longitudinal effect is mainly induced by two factors, namely the propeller additional water and the crankshaft structure. However, most of previous models were established with only one coupling factor, and consequently there is still a lack of a complete understanding for coupled torsional-longitudinal vibration. Hence, a comprehensive investigation is performed in this work to reveal deeper mechanisms of coupled torsional-longitudinal vibrations for marine propulsion shaft system. A discrete torsional-longitudinal vibration model is established for a real-life marine shaft. The coupling effects due to propeller additional water and dynamic characteristics of crankshaft are considered simultaneously to model the realistic vibration conditions. Then, a theoretical analysis is conducted on a simplified model to present a theoretical basis. Natural frequencies and forced steady-state responses are calculated numerically to analyze the influences of coupled torsional-longitudinal effect on the eigenvalue problem and vibration characteristics. Results show that, both the propeller additional water and the crankshaft structure could induce coupled torsional-longitudinal vibrations, and should be considered simultaneously in the model to achieve accurate vibration prediction and analysis. Besides, the coupling effect could induce a high amplitude beyond expectation that may even threaten the structure safe. The theoretical and numerical results in this study could provide some suggestions to designers and researchers attempting to obtain desirable vibration behaviors for marine propulsion shafts.
{"title":"Vibration Analysis of a Marine Propulsion Shaft System with the Torsional-longitudinal Coupling Effect Induced by Propeller and Crankshaft","authors":"Yang Yi, Zhang Lun, Yin Zhengyang, Wang Bozheng, Shen Guoji, Zhou Yang, Hu Niaoqing","doi":"10.1109/PHM-Nanjing52125.2021.9612978","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612978","url":null,"abstract":"Due to the complicated structure and harsh working environment, the marine propulsion shaft suffers from excessive vibrations in torsional, longitudinal and their coupled vibration modes. The coupled torsional-longitudinal effect is mainly induced by two factors, namely the propeller additional water and the crankshaft structure. However, most of previous models were established with only one coupling factor, and consequently there is still a lack of a complete understanding for coupled torsional-longitudinal vibration. Hence, a comprehensive investigation is performed in this work to reveal deeper mechanisms of coupled torsional-longitudinal vibrations for marine propulsion shaft system. A discrete torsional-longitudinal vibration model is established for a real-life marine shaft. The coupling effects due to propeller additional water and dynamic characteristics of crankshaft are considered simultaneously to model the realistic vibration conditions. Then, a theoretical analysis is conducted on a simplified model to present a theoretical basis. Natural frequencies and forced steady-state responses are calculated numerically to analyze the influences of coupled torsional-longitudinal effect on the eigenvalue problem and vibration characteristics. Results show that, both the propeller additional water and the crankshaft structure could induce coupled torsional-longitudinal vibrations, and should be considered simultaneously in the model to achieve accurate vibration prediction and analysis. Besides, the coupling effect could induce a high amplitude beyond expectation that may even threaten the structure safe. The theoretical and numerical results in this study could provide some suggestions to designers and researchers attempting to obtain desirable vibration behaviors for marine propulsion shafts.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134324606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-15DOI: 10.1109/PHM-Nanjing52125.2021.9612846
Xinda Chen, Minxiang Wei, Kai Chen, Yuhang Pei, Shunming Li
Digital lock-in amplifier(DLIA) is limited by its single-frequency signal detection nature, which makes it unable to detect multi-frequency signals. In order to make DLIA suitable for multi-frequency signals and broaden the application range of DLIA, a DLIA-based multi-frequency signal detection method implemented by field programmable gate array (FPGA) is proposed. This method reconstructs multi-frequency signals by improving DLIA and combining direct digital synthesizer (DDS). First, this paper introduces the principle of signal amplitude detection of DLIA, and then analyzes the detection process of the proposed method for multi-frequency signals. Then this paper describes the application process of the proposed method on FPGA. Finally, the bearing test was carried out, and the accurate identification of the defect signal was realized. The experimental results show that the multi-frequency DLIA signal detection method has the characteristics of stable output and strong anti-interference ability. The proposed method can effectively suppress noise and reconstruct bearing fault signals.
{"title":"A Method of Detecting Bearing Fault Signal Based on DLIA Implemented by FPGA","authors":"Xinda Chen, Minxiang Wei, Kai Chen, Yuhang Pei, Shunming Li","doi":"10.1109/PHM-Nanjing52125.2021.9612846","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612846","url":null,"abstract":"Digital lock-in amplifier(DLIA) is limited by its single-frequency signal detection nature, which makes it unable to detect multi-frequency signals. In order to make DLIA suitable for multi-frequency signals and broaden the application range of DLIA, a DLIA-based multi-frequency signal detection method implemented by field programmable gate array (FPGA) is proposed. This method reconstructs multi-frequency signals by improving DLIA and combining direct digital synthesizer (DDS). First, this paper introduces the principle of signal amplitude detection of DLIA, and then analyzes the detection process of the proposed method for multi-frequency signals. Then this paper describes the application process of the proposed method on FPGA. Finally, the bearing test was carried out, and the accurate identification of the defect signal was realized. The experimental results show that the multi-frequency DLIA signal detection method has the characteristics of stable output and strong anti-interference ability. The proposed method can effectively suppress noise and reconstruct bearing fault signals.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134510660","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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