Aiming at the problem that traditional single signal feature extraction algorithms cannot fully describe signal features, this paper proposes an ADS-B signal feature extraction and recognition method based on fusion entropy features. This article uses the ADS-B signal data set collected in a real environment as the original signal. There are a total of 198 airplanes in the data set, each with 200-600 samples, 3000 sampling points for each sample, and a sampling frequency of 50MHz. Eight planes out of 198 planes are randomly selected, 20 samples are randomly selected for each plane, and the singular spectrum entropy, wavelet energy spectrum entropy and Renyi entropy of the samples are extracted. The singular spectrum entropy and wavelet energy entropy, singular spectrum entropy and Renyi entropy are feature fusion respectively, and the random forest model is used as the classifier to classify the ADS-B signal. The recognition rate can reach 97.5%, which verifies the feasibility of the method.
{"title":"ADS-B Signal Recognition Method Based On Entropy Feature Fusion","authors":"Jialan Shen, Jingchao Li, Haijun Wang, Cheng Cong, Yulong Ying, Bin Zhang","doi":"10.1109/PHM-Nanjing52125.2021.9612770","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612770","url":null,"abstract":"Aiming at the problem that traditional single signal feature extraction algorithms cannot fully describe signal features, this paper proposes an ADS-B signal feature extraction and recognition method based on fusion entropy features. This article uses the ADS-B signal data set collected in a real environment as the original signal. There are a total of 198 airplanes in the data set, each with 200-600 samples, 3000 sampling points for each sample, and a sampling frequency of 50MHz. Eight planes out of 198 planes are randomly selected, 20 samples are randomly selected for each plane, and the singular spectrum entropy, wavelet energy spectrum entropy and Renyi entropy of the samples are extracted. The singular spectrum entropy and wavelet energy entropy, singular spectrum entropy and Renyi entropy are feature fusion respectively, and the random forest model is used as the classifier to classify the ADS-B signal. The recognition rate can reach 97.5%, which verifies the feasibility of the method.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"157 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":"116409369","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-Nanjing52125.2021.9612788
Kehong Lyu, Linxiao Wu, Xiaolong Wu, J. Qiu, Guanjun Liu
Electrical connectors are widely used in various types of shipboard equipment, in the marine salt spray environment, corrosion, especially micropore corrosion of electrical connector pins is an important degradation mode of electrical connectors, and the mechanism is complex. To address this problem, we analyzed the corrosion degradation mechanism of electrical connectors in salt spray environment, established a micropore corrosion model for different locations of electrical connector pin plating layer, analyzed the mechanism of corrosion stress on the static contact resistance of electrical connectors in the plugged state from a microscopic point of view, and finally carried out salt spray corrosion tests on electrical connectors to verily the correctness of the micropore corrosion model of electrical connectors under micropore corrosion conditions and the correctness of its contact degradation mechanism.
{"title":"Contact Degradation Mechanism and Test Study of Electrical Connector under Micropore Corrosion","authors":"Kehong Lyu, Linxiao Wu, Xiaolong Wu, J. Qiu, Guanjun Liu","doi":"10.1109/PHM-Nanjing52125.2021.9612788","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612788","url":null,"abstract":"Electrical connectors are widely used in various types of shipboard equipment, in the marine salt spray environment, corrosion, especially micropore corrosion of electrical connector pins is an important degradation mode of electrical connectors, and the mechanism is complex. To address this problem, we analyzed the corrosion degradation mechanism of electrical connectors in salt spray environment, established a micropore corrosion model for different locations of electrical connector pin plating layer, analyzed the mechanism of corrosion stress on the static contact resistance of electrical connectors in the plugged state from a microscopic point of view, and finally carried out salt spray corrosion tests on electrical connectors to verily the correctness of the micropore corrosion model of electrical connectors under micropore corrosion conditions and the correctness of its contact degradation mechanism.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"105 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":"116530790","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-Nanjing52125.2021.9612860
Yang Guan, Zong Meng, De-gang Sun
Rolling bearing is one of the main components of rotating machinery, timely and accurate fault diagnosis plays an important role in the reliability and safety of modern industrial systems. Under practical working conditions, normal data is abundant and the fault data is rare, the recognition rate of the minority class is low when the neural network is used to deal with these imbalanced datasets. Regarding the above-mentioned problems, a deep convolution fault diagnosis model based on ensemble learning voting method is proposed in this paper. First of all, the one-dimensional vibration signal was segmented through a sliding window for data enhancement. In the second place, the characteristics of the signals were extracted using deep convolutional neural networks. Finally, classification was carried out through the voting method of ensemble learning to realize fault diagnosis. The fault diagnosis models were tested on two different datasets and different imbalance ratios, and the experimental results show that the proposed method can be well applied in imbalanced datasets, which has higher fault recognition accuracy and faster operation.
{"title":"Fault Diagnosis of Rolling Bearing with Imbalanced Small Sample Scenarios","authors":"Yang Guan, Zong Meng, De-gang Sun","doi":"10.1109/PHM-Nanjing52125.2021.9612860","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612860","url":null,"abstract":"Rolling bearing is one of the main components of rotating machinery, timely and accurate fault diagnosis plays an important role in the reliability and safety of modern industrial systems. Under practical working conditions, normal data is abundant and the fault data is rare, the recognition rate of the minority class is low when the neural network is used to deal with these imbalanced datasets. Regarding the above-mentioned problems, a deep convolution fault diagnosis model based on ensemble learning voting method is proposed in this paper. First of all, the one-dimensional vibration signal was segmented through a sliding window for data enhancement. In the second place, the characteristics of the signals were extracted using deep convolutional neural networks. Finally, classification was carried out through the voting method of ensemble learning to realize fault diagnosis. The fault diagnosis models were tested on two different datasets and different imbalance ratios, and the experimental results show that the proposed method can be well applied in imbalanced datasets, which has higher fault recognition accuracy and faster operation.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"164 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":"122302218","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-Nanjing52125.2021.9612849
Dong-xia Liu
In the numerical simulation of highway tunnel excavation, it is difficult to calculate the yield criterion of rock and soil in traditional calculation, which leads to the lack of individual parameters in the final numerical simulation results, resulting in the low overall stability of tunnel deep foundation section, rebound rate of deep foundation pit bottom and limit equilibrium rate of supporting pile. Therefore, a numerical simulation analysis method of highway tunnel excavation based on artificial intelligence algorithm is proposed. Firstly, the damage stage method is used to confirm the surrounding rock pressure parameters. After confirming the stability of the surrounding rock, the seismic factors of the highway tunnel are considered to determine the buried depth of the tunnel. The yield function is used to calculate the yield criterion of geotechnical materials. Finally, the artificial intelligence algorithm is used for numerical simulation. The simulation results show that the overall stability of the tunnel deep foundation section, the rebound rate of the deep foundation pit bottom and the limit equilibrium rate of the supporting pile are high.
{"title":"Numerical Simulation Analysis of Highway Tunnel Excavation Based on Artificial Intelligence Algorithm","authors":"Dong-xia Liu","doi":"10.1109/PHM-Nanjing52125.2021.9612849","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612849","url":null,"abstract":"In the numerical simulation of highway tunnel excavation, it is difficult to calculate the yield criterion of rock and soil in traditional calculation, which leads to the lack of individual parameters in the final numerical simulation results, resulting in the low overall stability of tunnel deep foundation section, rebound rate of deep foundation pit bottom and limit equilibrium rate of supporting pile. Therefore, a numerical simulation analysis method of highway tunnel excavation based on artificial intelligence algorithm is proposed. Firstly, the damage stage method is used to confirm the surrounding rock pressure parameters. After confirming the stability of the surrounding rock, the seismic factors of the highway tunnel are considered to determine the buried depth of the tunnel. The yield function is used to calculate the yield criterion of geotechnical materials. Finally, the artificial intelligence algorithm is used for numerical simulation. The simulation results show that the overall stability of the tunnel deep foundation section, the rebound rate of the deep foundation pit bottom and the limit equilibrium rate of the supporting pile are high.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"8 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":"121977534","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-Nanjing52125.2021.9613088
Guangquan Zhao, Yongning Zhang, Kankan Wu, Jun Zhou
Rotating machinery is widely used in modern industrial technology. Timely diagnosis of faults of rotating machinery equipment is of great significance to maintain the reliability and safety of the whole system. Since the development of fault diagnosis technology, there have been many diagnosis methods that can be applied to rotating machinery, and these methods have achieved good results. However, many of these methods cannot balance the relationship between diagnostic accuracy and timeliness very well, and require high computing capabilities of the device, which is not conducive to algorithm deployment on hardware devices, and the long diagnosis time is not conducive to real-time monitoring of the rotating machinery. This paper takes the core component bearing of rotating machinery equipment as the object, and proposes a fault diagnosis method for rotating machinery based on light gradient boosting machine (LightGBM). In this paper, two kinds of bearing data sets are used for ten-fold cross-validation, which can achieve high accuracy and very short training time. The experimental results show that LightGBM has higher diagnostic accuracy and better real-time performance.
{"title":"Rotating Machinery Fault Diagnosis using Light Gradient Boosting Machine","authors":"Guangquan Zhao, Yongning Zhang, Kankan Wu, Jun Zhou","doi":"10.1109/PHM-Nanjing52125.2021.9613088","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9613088","url":null,"abstract":"Rotating machinery is widely used in modern industrial technology. Timely diagnosis of faults of rotating machinery equipment is of great significance to maintain the reliability and safety of the whole system. Since the development of fault diagnosis technology, there have been many diagnosis methods that can be applied to rotating machinery, and these methods have achieved good results. However, many of these methods cannot balance the relationship between diagnostic accuracy and timeliness very well, and require high computing capabilities of the device, which is not conducive to algorithm deployment on hardware devices, and the long diagnosis time is not conducive to real-time monitoring of the rotating machinery. This paper takes the core component bearing of rotating machinery equipment as the object, and proposes a fault diagnosis method for rotating machinery based on light gradient boosting machine (LightGBM). In this paper, two kinds of bearing data sets are used for ten-fold cross-validation, which can achieve high accuracy and very short training time. The experimental results show that LightGBM has higher diagnostic accuracy and better real-time performance.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"32 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":"122103405","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-Nanjing52125.2021.9613122
Wang Ying, Wu Jie
The amplitude and phase stability, and delay characteristics between channels affect overall performance of digital phased array radar array. Aimed at the shortcoming of usual broadband signal processing method test precision, two methods are introduced and compared, including time domain interpolation of matched filtering and frequency domain interpolation of dechirping processing. This paper set up broadband signal on-line testing system. Test module extracts the channels amplitude and time-delay after processing. After verification and comparison, frequency domain interpolation of dechirping processing is proved to promote broadband signal performance testing accuracy of digital phased array radar.
{"title":"Radar Broadband Signal High-precision On-line Testing Method","authors":"Wang Ying, Wu Jie","doi":"10.1109/PHM-Nanjing52125.2021.9613122","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9613122","url":null,"abstract":"The amplitude and phase stability, and delay characteristics between channels affect overall performance of digital phased array radar array. Aimed at the shortcoming of usual broadband signal processing method test precision, two methods are introduced and compared, including time domain interpolation of matched filtering and frequency domain interpolation of dechirping processing. This paper set up broadband signal on-line testing system. Test module extracts the channels amplitude and time-delay after processing. After verification and comparison, frequency domain interpolation of dechirping processing is proved to promote broadband signal performance testing accuracy of digital phased array radar.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122191206","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}
With the continuous improvement of the aircraft environmental control system, the content of maintenance manuals on which the maintenance work is based are constantly enriched, causing inconvenience of quick fault positioning. Maintenance engineers’ experience and knowledge are often required and the labor cost of maintenance work increases. For improving the utilization efficiency of these resources, this paper uses the deep text matching model, BERT, to extract semantic information in the maintenance record provided by China Eastern Airlines. After obtaining the entities of warnings and fault causes and the relationship between them, a knowledge graph for fault diagnosis of civil aircraft environmental control system is constructed. And a fault diagnosis support algorithm is completed, which is conducive to improving fault location and reducing aircraft maintenance costs.
{"title":"Knowledge Graph Construction for Fault Diagnosis of Aircraft Environmental Control System","authors":"Shutong Zhang, Yini Zhang, Yongsheng Yang, Wei Cheng, Honghua Zhao, Yuanxiang Li","doi":"10.1109/PHM-Nanjing52125.2021.9613135","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9613135","url":null,"abstract":"With the continuous improvement of the aircraft environmental control system, the content of maintenance manuals on which the maintenance work is based are constantly enriched, causing inconvenience of quick fault positioning. Maintenance engineers’ experience and knowledge are often required and the labor cost of maintenance work increases. For improving the utilization efficiency of these resources, this paper uses the deep text matching model, BERT, to extract semantic information in the maintenance record provided by China Eastern Airlines. After obtaining the entities of warnings and fault causes and the relationship between them, a knowledge graph for fault diagnosis of civil aircraft environmental control system is constructed. And a fault diagnosis support algorithm is completed, which is conducive to improving fault location and reducing aircraft maintenance costs.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"3 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":"129331226","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-Nanjing52125.2021.9612961
Min Qin, Huai-hai Chen
Neural network can mine data features, and has strong anti-noise ability and applicability. The operational modal analysis (OMA) method based on back propagation neural network (BPNN) is proposed in this paper. Firstly, the dataset is preprocessed based on the input and output functions, which increases the anti-noise ability of the proposed method and simplifies the training by reducing the model parameters. Secondly, a three-layer BP neural network is established to identify parameters as accurately as possible with minimal network complexity and training data. In addition, an improved resilient back propagation (RPROP) algorithm is a fast and accurate batch learning methods for neural networks, which is used in the BPNN. Finally, simulation and experimental results show that the superior learning capabilities of BPNN even with few neurons and hidden layers. The proposed method has the advantages of high accuracy, strong generalization ability and fast convergence speed.
{"title":"Operational modal analysis based on neural network with singular value decomposition","authors":"Min Qin, Huai-hai Chen","doi":"10.1109/PHM-Nanjing52125.2021.9612961","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612961","url":null,"abstract":"Neural network can mine data features, and has strong anti-noise ability and applicability. The operational modal analysis (OMA) method based on back propagation neural network (BPNN) is proposed in this paper. Firstly, the dataset is preprocessed based on the input and output functions, which increases the anti-noise ability of the proposed method and simplifies the training by reducing the model parameters. Secondly, a three-layer BP neural network is established to identify parameters as accurately as possible with minimal network complexity and training data. In addition, an improved resilient back propagation (RPROP) algorithm is a fast and accurate batch learning methods for neural networks, which is used in the BPNN. Finally, simulation and experimental results show that the superior learning capabilities of BPNN even with few neurons and hidden layers. The proposed method has the advantages of high accuracy, strong generalization ability and fast convergence speed.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"19 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":"129446497","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-Nanjing52125.2021.9612883
Yahan Yu, Juan Du, Guanghao Ren, Y. Tan, Jian Wang, Guigang Zhang
Aero-mechanical parts are an important part of the aircraft, and the maintenance of their failures also consumes a lot of manpower and financial resources. Therefore, the fault diagnosis research of aero-mechanical parts is of great significance for ensuring the safety of human life and reducing economic losses. With the development of fault diagnosis technology, the monitoring data is becoming more and more abundant and complex. The traditional methods of processing and analyzing the monitoring data have become more difficult, and it is difficult to establish accurate mathematical models. Therefore, the rapid diagnosis method of aviation machinery parts Become the research focus of fault diagnosis. This paper constructs a rapid fault diagnosis system for the construction of aviation machinery parts. Based on the input of past cases, new cases, literature cases, and book knowledge, the case library is refined and the graph library and rule term library are added. AR algorithm is used to mine and obtain Useful association rules between the decision attributes (failure mode, failure mechanism, failure reason, etc.) of the failure information in the database and the basic attributes (basic information other than the decision attributes), to achieve the purpose of assisting failure analysts in rapid fault diagnosis.
{"title":"Fast Fault Diagnosis System Based on Data Mining AR Algorithm","authors":"Yahan Yu, Juan Du, Guanghao Ren, Y. Tan, Jian Wang, Guigang Zhang","doi":"10.1109/PHM-Nanjing52125.2021.9612883","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612883","url":null,"abstract":"Aero-mechanical parts are an important part of the aircraft, and the maintenance of their failures also consumes a lot of manpower and financial resources. Therefore, the fault diagnosis research of aero-mechanical parts is of great significance for ensuring the safety of human life and reducing economic losses. With the development of fault diagnosis technology, the monitoring data is becoming more and more abundant and complex. The traditional methods of processing and analyzing the monitoring data have become more difficult, and it is difficult to establish accurate mathematical models. Therefore, the rapid diagnosis method of aviation machinery parts Become the research focus of fault diagnosis. This paper constructs a rapid fault diagnosis system for the construction of aviation machinery parts. Based on the input of past cases, new cases, literature cases, and book knowledge, the case library is refined and the graph library and rule term library are added. AR algorithm is used to mine and obtain Useful association rules between the decision attributes (failure mode, failure mechanism, failure reason, etc.) of the failure information in the database and the basic attributes (basic information other than the decision attributes), to achieve the purpose of assisting failure analysts in rapid fault diagnosis.","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":"129874487","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-Nanjing52125.2021.9613009
Guang Miao, Shanshui Yang, Li Wang, Dehong Li, Hongqi Jiang
The reliable operation of the aircraft power supply system is an important factor in ensuring the safe flight of the aircraft. Taking an aircraft HVDC power supply system as an example, a reliability assessment method suitable for large aircraft power supply networks is introduced. With the goal of ensuring that all bus bars are supplied with power, the method uses graph theory and other related knowledge to transform the aircraft power supply network into a cyclic directed rooted communication network. Thereby converting the problem into solving the network%s rooted communication reliability; then using factoring algorithm performs binary tree decomposition on the generated rooted communication network to gradually reduce the network scale, and put forward the rules for selecting points that can improve the efficiency of the algorithm. Finally, in the process of generating the binary tree, the overall reliability function of the network can be obtained, so that typical reference indicators such as reliability and MTBF can be calculated.
{"title":"Reliability Evaluation of Aircraft Power Supply System Based on Factoring Algorithm","authors":"Guang Miao, Shanshui Yang, Li Wang, Dehong Li, Hongqi Jiang","doi":"10.1109/PHM-Nanjing52125.2021.9613009","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9613009","url":null,"abstract":"The reliable operation of the aircraft power supply system is an important factor in ensuring the safe flight of the aircraft. Taking an aircraft HVDC power supply system as an example, a reliability assessment method suitable for large aircraft power supply networks is introduced. With the goal of ensuring that all bus bars are supplied with power, the method uses graph theory and other related knowledge to transform the aircraft power supply network into a cyclic directed rooted communication network. Thereby converting the problem into solving the network%s rooted communication reliability; then using factoring algorithm performs binary tree decomposition on the generated rooted communication network to gradually reduce the network scale, and put forward the rules for selecting points that can improve the efficiency of the algorithm. Finally, in the process of generating the binary tree, the overall reliability function of the network can be obtained, so that typical reference indicators such as reliability and MTBF can be calculated.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"20 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":"130350667","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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