@inproceedings{wang_railway_2020,
	title = {Railway {Wagon} {Wheelset} {Fault} {Diagnosis} {Method} {Based} on {DBN}},
	doi = {10.1109/PHM-Shanghai49105.2020.9280980},
	abstract = {Wheelset is a crucial part of the operation and braking part of railway wagons, and its failure will affect the operation safety of the entire railway system. And fault diagnosis is an important basis for realizing railway scientific maintenance decisions. Learn the method of deep belief network (DBN) to process and diagnose fault data of freighters. The paper uses dynamic factors to optimize the update mechanism of particle velocity, proposes an active factor controlled particle swarm algorithm (APSO), and uses the APSO algorithm to optimize the selection of DBN weights to form a round-pair fault detection algorithm (APSO-DBN) This speeds up DBN training and improves the overall performance of the diagnostic model. The experimental steps and the effectiveness of the method are verified by experiments.},
	booktitle = {2020 {Global} {Reliability} and {Prognostics} and {Health} {Management} ({PHM}-{Shanghai})},
	author = {Wang, Hongkun and Li, Honghui and Li, Yusheng and Duan, Yuhang},
	month = oct,
	year = {2020},
	keywords = {Classification algorithms, Fault detection, Fault diagnosis, Heuristic algorithms, Particle swarm optimization, Rail transportation, Training, deep belief network, deep learning, fault detection, particle swarm algorithm, railway freighter wheelset},
	pages = {1--6},
}

{"_id":"ACLzXKZQEYapQD8Kz","bibbaseid":"wang-li-li-duan-railwaywagonwheelsetfaultdiagnosismethodbasedondbn-2020","author_short":["Wang, H.","Li, H.","Li, Y.","Duan, Y."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","title":"Railway Wagon Wheelset Fault Diagnosis Method Based on DBN","doi":"10.1109/PHM-Shanghai49105.2020.9280980","abstract":"Wheelset is a crucial part of the operation and braking part of railway wagons, and its failure will affect the operation safety of the entire railway system. And fault diagnosis is an important basis for realizing railway scientific maintenance decisions. Learn the method of deep belief network (DBN) to process and diagnose fault data of freighters. The paper uses dynamic factors to optimize the update mechanism of particle velocity, proposes an active factor controlled particle swarm algorithm (APSO), and uses the APSO algorithm to optimize the selection of DBN weights to form a round-pair fault detection algorithm (APSO-DBN) This speeds up DBN training and improves the overall performance of the diagnostic model. The experimental steps and the effectiveness of the method are verified by experiments.","booktitle":"2020 Global Reliability and Prognostics and Health Management (PHM-Shanghai)","author":[{"propositions":[],"lastnames":["Wang"],"firstnames":["Hongkun"],"suffixes":[]},{"propositions":[],"lastnames":["Li"],"firstnames":["Honghui"],"suffixes":[]},{"propositions":[],"lastnames":["Li"],"firstnames":["Yusheng"],"suffixes":[]},{"propositions":[],"lastnames":["Duan"],"firstnames":["Yuhang"],"suffixes":[]}],"month":"October","year":"2020","keywords":"Classification algorithms, Fault detection, Fault diagnosis, Heuristic algorithms, Particle swarm optimization, Rail transportation, Training, deep belief network, deep learning, fault detection, particle swarm algorithm, railway freighter wheelset","pages":"1–6","bibtex":"@inproceedings{wang_railway_2020,\n\ttitle = {Railway {Wagon} {Wheelset} {Fault} {Diagnosis} {Method} {Based} on {DBN}},\n\tdoi = {10.1109/PHM-Shanghai49105.2020.9280980},\n\tabstract = {Wheelset is a crucial part of the operation and braking part of railway wagons, and its failure will affect the operation safety of the entire railway system. And fault diagnosis is an important basis for realizing railway scientific maintenance decisions. Learn the method of deep belief network (DBN) to process and diagnose fault data of freighters. The paper uses dynamic factors to optimize the update mechanism of particle velocity, proposes an active factor controlled particle swarm algorithm (APSO), and uses the APSO algorithm to optimize the selection of DBN weights to form a round-pair fault detection algorithm (APSO-DBN) This speeds up DBN training and improves the overall performance of the diagnostic model. The experimental steps and the effectiveness of the method are verified by experiments.},\n\tbooktitle = {2020 {Global} {Reliability} and {Prognostics} and {Health} {Management} ({PHM}-{Shanghai})},\n\tauthor = {Wang, Hongkun and Li, Honghui and Li, Yusheng and Duan, Yuhang},\n\tmonth = oct,\n\tyear = {2020},\n\tkeywords = {Classification algorithms, Fault detection, Fault diagnosis, Heuristic algorithms, Particle swarm optimization, Rail transportation, Training, deep belief network, deep learning, fault detection, particle swarm algorithm, railway freighter wheelset},\n\tpages = {1--6},\n}\n\n\n\n","author_short":["Wang, H.","Li, H.","Li, Y.","Duan, Y."],"key":"wang_railway_2020","id":"wang_railway_2020","bibbaseid":"wang-li-li-duan-railwaywagonwheelsetfaultdiagnosismethodbasedondbn-2020","role":"author","urls":{},"keyword":["Classification algorithms","Fault detection","Fault diagnosis","Heuristic algorithms","Particle swarm optimization","Rail transportation","Training","deep belief network","deep learning","fault detection","particle swarm algorithm","railway freighter wheelset"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"inproceedings","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["yanwtMpCcFaHzRwWb","iwKepCrWBps7ojhDx"],"keywords":["classification algorithms","fault detection","fault diagnosis","heuristic algorithms","particle swarm optimization","rail transportation","training","deep belief network","deep learning","fault detection","particle swarm algorithm","railway freighter wheelset"],"search_terms":["railway","wagon","wheelset","fault","diagnosis","method","based","dbn","wang","li","li","duan"],"title":"Railway Wagon Wheelset Fault Diagnosis Method Based on DBN","year":2020}