In this paper, a developed risk-based methodology for optimizing the different inspected regions of pipeline networks is proposed to provide the reasonable and quantitative risk information for decision makers. This method established a novel risk model, which combines spatial statistical technology and risk assessment model to simulate the risk level of all parts of pipeline network, so as to give priority to pipeline network inspection. The risk in different regions of the pipeline network is derived from the product of failure probability and failure severity in our approach. As a case study, the inspection priority of a city's sewer network is carried out according to our method and, consequently, the feasibility of this method is verified.
{"title":"A Spatial Statistical Approach for Risk-based Inspection of Sewer Network","authors":"Yangjie Zhang, H. Mu, X. Yi","doi":"10.1109/SDPC.2019.00171","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00171","url":null,"abstract":"In this paper, a developed risk-based methodology for optimizing the different inspected regions of pipeline networks is proposed to provide the reasonable and quantitative risk information for decision makers. This method established a novel risk model, which combines spatial statistical technology and risk assessment model to simulate the risk level of all parts of pipeline network, so as to give priority to pipeline network inspection. The risk in different regions of the pipeline network is derived from the product of failure probability and failure severity in our approach. As a case study, the inspection priority of a city's sewer network is carried out according to our method and, consequently, the feasibility of this method is verified.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132080911","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}
P. Zhao, Hongchao Wu, Xue Shi, Jing Li, X. Yi, Shulin Liu
In the process of repair, maintenance and use of the current equipment, there are problems such as complicated structure and lack of visual guidance, but the equipment maintenance guiding system based on augmented reality technology provides a good solution. Based on the research results of augmented reality equipment maintenance technology at home and abroad, this paper proposed a C/S architecture maintenance induction system framework, by using Microsoft Hololens glasses as the hardware platform and using augmented reality, artificial intelligence recognition, spatial positioning technology, designed and discussed a remote control tower maintenance and induction system. The example proves that the augmented reality maintenance induction system has a good application effect in complex equipment maintenance and training.
{"title":"Research on maintenance guiding system based on augmented reality","authors":"P. Zhao, Hongchao Wu, Xue Shi, Jing Li, X. Yi, Shulin Liu","doi":"10.1109/SDPC.2019.00180","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00180","url":null,"abstract":"In the process of repair, maintenance and use of the current equipment, there are problems such as complicated structure and lack of visual guidance, but the equipment maintenance guiding system based on augmented reality technology provides a good solution. Based on the research results of augmented reality equipment maintenance technology at home and abroad, this paper proposed a C/S architecture maintenance induction system framework, by using Microsoft Hololens glasses as the hardware platform and using augmented reality, artificial intelligence recognition, spatial positioning technology, designed and discussed a remote control tower maintenance and induction system. The example proves that the augmented reality maintenance induction system has a good application effect in complex equipment maintenance and training.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130625127","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}
This study proposes a method, building a system reliability model using historical data, to produce an accurate estimation of system reliability. This method employs an optimal interpolation function, generated by optimization theory and historical data, to estimate system reliability. In this paper, the method is provided with a detailed explanation and an evaluation with an experiment. The evaluation indicates that the method can build the objective model and estimate the system reliability accurately with the growth of the amount of historical data.
{"title":"A New Method For Estimating System Reliability Using Historical Data","authors":"Yue Chen, P. Hou, X. Yi","doi":"10.1109/SDPC.2019.00103","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00103","url":null,"abstract":"This study proposes a method, building a system reliability model using historical data, to produce an accurate estimation of system reliability. This method employs an optimal interpolation function, generated by optimization theory and historical data, to estimate system reliability. In this paper, the method is provided with a detailed explanation and an evaluation with an experiment. The evaluation indicates that the method can build the objective model and estimate the system reliability accurately with the growth of the amount of historical data.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130641630","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}
Safety issues in hybrid systems increasingly result from a combination of multiple categories of hazard factors. However, current hazard analysis methods mainly focus on just one kind of system hazard. This paper presents a model-based safety analysis method for hybrid system that is based on multiple hazard factor categories. Using a Model-Based Safety Analysis framework, the functional model of a hybrid system is first established. Its logic component is modeled using a state diagram and its continuous component is modeled using a transfer function or a state equation. The safety model of the system is then constructed considering a variety of hazard factors. Improper control and logic inconsistency are established for the logic component. Component deviation and logic inconsistency are established for the continuous component. Finally, the safety of the system is then analyzed and the hazard paths are obtained. As an example, the Wheel Brake System for an aircraft is taken to analyze the safety of the aircraft landing phase. By taking into account multiple hazard factor categories, we are able to identify hazard paths that could not be found using a single hazard factor category.
{"title":"A Model-Based Safety Analysis Method for Hybrid Systems Based on Multiple Hazard Factor Categories","authors":"Hongya Wang, D. Zhong, Yukun Zhao, Rui Sun","doi":"10.1109/SDPC.2019.00181","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00181","url":null,"abstract":"Safety issues in hybrid systems increasingly result from a combination of multiple categories of hazard factors. However, current hazard analysis methods mainly focus on just one kind of system hazard. This paper presents a model-based safety analysis method for hybrid system that is based on multiple hazard factor categories. Using a Model-Based Safety Analysis framework, the functional model of a hybrid system is first established. Its logic component is modeled using a state diagram and its continuous component is modeled using a transfer function or a state equation. The safety model of the system is then constructed considering a variety of hazard factors. Improper control and logic inconsistency are established for the logic component. Component deviation and logic inconsistency are established for the continuous component. Finally, the safety of the system is then analyzed and the hazard paths are obtained. As an example, the Wheel Brake System for an aircraft is taken to analyze the safety of the aircraft landing phase. By taking into account multiple hazard factor categories, we are able to identify hazard paths that could not be found using a single hazard factor category.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126651288","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}
A new spectrum envelope estimation method for use in rotary machine monitoring is proposed. Aspects including the Internet of things, monitoring, sensing and signal processing have been considered for diagnostics of low-speed rotary machines. In this paper, signal processing in sensing when using a laser Doppler velocimeter for monitoring of low–rotational-speed machines such as gear motors is studied. It is possible to measure the shaft vibration of a rotary machine directly via laser Doppler velocimetry. Therefore, this type of velocimeter is suitable for replacement of the current monitoring methods used in vibration measurement of low-rotational-speed machines. However, the laser Doppler velocimeter shows low sensitivity to the harmonic spectrum structure, which is the most important information required when monitoring a gear structure. The balance of each harmonic spectrum structure can be confirmed using a spectral envelope. Therefore, a signal processing technique that addresses the problem of low sensitivity to harmonic components during spectral envelope estimation is proposed. By focusing on the peak frequency of the spectrum, which is a major technique in the signal processing field, a method for construction of an autocorrelation function and estimation of the spectrum envelope is proposed. The proposed spectral envelope estimation method is verified by an illustration using the gear meshing frequency. The effectiveness of the proposed estimation method is confirmed using a visualization graph.
{"title":"Estimation of Spectrum Envelope for Gear Motor Monitoring Using A Laser Doppler Velocimeter","authors":"Kesaaki Minemura, Toshiya Nagumo, T. Akashi","doi":"10.1109/SDPC.2019.00050","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00050","url":null,"abstract":"A new spectrum envelope estimation method for use in rotary machine monitoring is proposed. Aspects including the Internet of things, monitoring, sensing and signal processing have been considered for diagnostics of low-speed rotary machines. In this paper, signal processing in sensing when using a laser Doppler velocimeter for monitoring of low–rotational-speed machines such as gear motors is studied. It is possible to measure the shaft vibration of a rotary machine directly via laser Doppler velocimetry. Therefore, this type of velocimeter is suitable for replacement of the current monitoring methods used in vibration measurement of low-rotational-speed machines. However, the laser Doppler velocimeter shows low sensitivity to the harmonic spectrum structure, which is the most important information required when monitoring a gear structure. The balance of each harmonic spectrum structure can be confirmed using a spectral envelope. Therefore, a signal processing technique that addresses the problem of low sensitivity to harmonic components during spectral envelope estimation is proposed. By focusing on the peak frequency of the spectrum, which is a major technique in the signal processing field, a method for construction of an autocorrelation function and estimation of the spectrum envelope is proposed. The proposed spectral envelope estimation method is verified by an illustration using the gear meshing frequency. The effectiveness of the proposed estimation method is confirmed using a visualization graph.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114300638","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}
The ultra-high frequency (UHF) signal based partial discharge localization method has not been widely used in substations due to the difficulty in signal sampling. The frequency range of partial discharge UHF signal is from 300MHz to 3GHz, which has exceeded the sampling range of most synchronous multi-channel sampling devices. In this paper, a novel partial discharge UHF signal sampling method based on compressive is proposed. The random Gaussian matrix is used as the measurement matrix and the Orthogonal Matching Pursuit (OMP) algorithm is used as the signal reconstruction algorithm. The sparse partial discharge signal is captured and represented at a rate significantly below the Nyquist rate. Simulation tests and field tests have verified the feasibility of the proposed method. The sampling frequency of the UHF partial discharge signal is reduced from 2.5GS/s to 550MS/s in simulation tests and is reduced from 4GS/s to 800MS/s in field tests.
{"title":"UHF Partial Discharge Signal Recovery Method Based on Compressive Sensing","authors":"Q. Gao, Qi Liu, Zhannan Guo, Nan Zhou, Zhen Li","doi":"10.1109/SDPC.2019.00152","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00152","url":null,"abstract":"The ultra-high frequency (UHF) signal based partial discharge localization method has not been widely used in substations due to the difficulty in signal sampling. The frequency range of partial discharge UHF signal is from 300MHz to 3GHz, which has exceeded the sampling range of most synchronous multi-channel sampling devices. In this paper, a novel partial discharge UHF signal sampling method based on compressive is proposed. The random Gaussian matrix is used as the measurement matrix and the Orthogonal Matching Pursuit (OMP) algorithm is used as the signal reconstruction algorithm. The sparse partial discharge signal is captured and represented at a rate significantly below the Nyquist rate. Simulation tests and field tests have verified the feasibility of the proposed method. The sampling frequency of the UHF partial discharge signal is reduced from 2.5GS/s to 550MS/s in simulation tests and is reduced from 4GS/s to 800MS/s in field tests.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123323266","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}
CBR (Case-based Reasoning) is an method in the field of artificial intelligence. This paper describes the application of CBR method in the educational administration system. Five groups of evaluation data were selected during the course of a class,through normalization, fuzzy clustering, and similarity metrics,match the source case with the highest similarity to the target case. The source case consists of 37 students in this class. The target case is a classmate from another class. Students in the target case can use the review plan of the classmates who match the most similar students in the source case. In addition, the intelligent module of the educational administration system is implemented under the jBPM framework. This platform workflow engine can drive the flow of each link, more conducive to intelligence is to achieve.
{"title":"CBR Theory Applied to Educational Administration System based on jBPM","authors":"Feng He, Q. Han","doi":"10.1109/SDPC.2019.00095","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00095","url":null,"abstract":"CBR (Case-based Reasoning) is an method in the field of artificial intelligence. This paper describes the application of CBR method in the educational administration system. Five groups of evaluation data were selected during the course of a class,through normalization, fuzzy clustering, and similarity metrics,match the source case with the highest similarity to the target case. The source case consists of 37 students in this class. The target case is a classmate from another class. Students in the target case can use the review plan of the classmates who match the most similar students in the source case. In addition, the intelligent module of the educational administration system is implemented under the jBPM framework. This platform workflow engine can drive the flow of each link, more conducive to intelligence is to achieve.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129813149","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}
Z. Peng, W. Gao, Yong Li, Mingjun Ding, Weizhao Chao, Xiaoling Hu, Hua Guo
A model is proposed in this paper to study the influential power of steady-state scam parameters of terminalsensitive submunition on the target. Then simulation is performed by using non-linear programming under Matlab. The steady-state scan parameters of terminal-sensitive submunition are constant and the scan law is fixed. As a result of this, there may be the problem of missed scan or even failure to recognize the target. This study proposes a self-adaptive adjustment method based on target velocity and length. A relational model is built between the scan parameters and scan target of the terminal-sensitive submunition. The initial scan angle and steady-state scan angle are determined under Matlab. Simulation results show that the proposed self-adaptive adjustment method can rapidly adjust the scan angle, increase the scanning efficiency and timely recognize the target. Our study provides theoretical support and reference data for the design of terminal-sensitive submunition.
{"title":"An optimization study of self-adaptive target parameter scan technology for terminal-sensitive submunition","authors":"Z. Peng, W. Gao, Yong Li, Mingjun Ding, Weizhao Chao, Xiaoling Hu, Hua Guo","doi":"10.1109/SDPC.2019.00066","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00066","url":null,"abstract":"A model is proposed in this paper to study the influential power of steady-state scam parameters of terminalsensitive submunition on the target. Then simulation is performed by using non-linear programming under Matlab. The steady-state scan parameters of terminal-sensitive submunition are constant and the scan law is fixed. As a result of this, there may be the problem of missed scan or even failure to recognize the target. This study proposes a self-adaptive adjustment method based on target velocity and length. A relational model is built between the scan parameters and scan target of the terminal-sensitive submunition. The initial scan angle and steady-state scan angle are determined under Matlab. Simulation results show that the proposed self-adaptive adjustment method can rapidly adjust the scan angle, increase the scanning efficiency and timely recognize the target. Our study provides theoretical support and reference data for the design of terminal-sensitive submunition.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125272933","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}
Electrical equipment failure is a monotonous process with a sudden change in performance due to its own life cycle and a sudden change in state deterioration. Taking the dedicated power supply for tank commander panoramic of a certain type tank as the research object. The concepts of grey prediction model and Markov model are introduced. The Markov model is used to classify the residuals of the grey GM(1,1) prediction model, and the state transition matrix is determined. The particle swarm optimization algorithm is used to find the whitening coefficient of the Markov model residual state, and the grey Markov model of particle swarm optimization is established.The dedicated power supply for tank commander panoramic measurement values (highest value, lowest value, median value) are predicted. The results show that the optimized prediction results are better than the original gray GM (1,1) and gray Markov prediction methods. With the increase of the working time of the dedicated power supply of the dedicated power supply for tank commander panoramic, the trend of relative error occurrence state transition is more stable, the accuracy of prediction will be further improved, and the equipment can predict and maintain a certain value for maintenance work.
{"title":"Prediction method of dedicated power supply for tank commander panoramic based on grey Markov model optimized by PSO","authors":"Yingshun Li, Yu Xiao, X. Yi","doi":"10.1109/SDPC.2019.00043","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00043","url":null,"abstract":"Electrical equipment failure is a monotonous process with a sudden change in performance due to its own life cycle and a sudden change in state deterioration. Taking the dedicated power supply for tank commander panoramic of a certain type tank as the research object. The concepts of grey prediction model and Markov model are introduced. The Markov model is used to classify the residuals of the grey GM(1,1) prediction model, and the state transition matrix is determined. The particle swarm optimization algorithm is used to find the whitening coefficient of the Markov model residual state, and the grey Markov model of particle swarm optimization is established.The dedicated power supply for tank commander panoramic measurement values (highest value, lowest value, median value) are predicted. The results show that the optimized prediction results are better than the original gray GM (1,1) and gray Markov prediction methods. With the increase of the working time of the dedicated power supply of the dedicated power supply for tank commander panoramic, the trend of relative error occurrence state transition is more stable, the accuracy of prediction will be further improved, and the equipment can predict and maintain a certain value for maintenance work.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129177023","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}
The valve train fault is a common mechanical fault of internal combustion engines (ICEs) due to the valve clearance usually oversized because of the wear of valve mechanism, material deformations, and long continuous running hours. Feature extraction dependent on the expertise and experience too much in traditional fault diagnosis. In this study, a stacked autoencoder (SAE) is proposed for adaptive and hierarchical feature extraction in cylinder vibration signals. The capability of feature mining in SAE is enhanced after unsupervised layer-by-layer pre-training and supervised fine-tuning. Further, the dropout trick and the batch normalization trick are introduced to prevent over-fitting and accelerate model convergence. The harmonic search (HS) algorithm is proposed to obtain the optimal hyper-parameter values in the SAE model, and achieve adaptive adjustment of the model structure. The diesel engine vibration data consisting of seven valve health states is employed to verify the effectiveness of the proposed method, the results demonstrate that the proposed method outperforms original SAE and many conventional fault diagnosis algorithms in terms of the classification accuracy.
{"title":"Valve fault diagnosis of internal combustion engine based on an improved stacked autoencoder","authors":"Kun Chen, Zhiwei Mao, Haipeng Zhao, Jinjie Zhang","doi":"10.1109/SDPC.2019.00060","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00060","url":null,"abstract":"The valve train fault is a common mechanical fault of internal combustion engines (ICEs) due to the valve clearance usually oversized because of the wear of valve mechanism, material deformations, and long continuous running hours. Feature extraction dependent on the expertise and experience too much in traditional fault diagnosis. In this study, a stacked autoencoder (SAE) is proposed for adaptive and hierarchical feature extraction in cylinder vibration signals. The capability of feature mining in SAE is enhanced after unsupervised layer-by-layer pre-training and supervised fine-tuning. Further, the dropout trick and the batch normalization trick are introduced to prevent over-fitting and accelerate model convergence. The harmonic search (HS) algorithm is proposed to obtain the optimal hyper-parameter values in the SAE model, and achieve adaptive adjustment of the model structure. The diesel engine vibration data consisting of seven valve health states is employed to verify the effectiveness of the proposed method, the results demonstrate that the proposed method outperforms original SAE and many conventional fault diagnosis algorithms in terms of the classification accuracy.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128335199","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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