Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks. Zhao, R., Wang, D., Yan, R., Mao, K., Shen, F., & Wang, J. IEEE Transactions on Industrial Electronics, 65(2):1539–1548, February, 2018. Conference Name: IEEE Transactions on Industrial Electronicsdoi abstract bibtex In modern industries, machine health monitoring systems (MHMS) have been applied wildly with the goal of realizing predictive maintenance including failures tracking, downtime reduction, and assets preservation. In the era of big machinery data, data-driven MHMS have achieved remarkable results in the detection of faults after the occurrence of certain failures (diagnosis) and prediction of the future working conditions and the remaining useful life (prognosis). The numerical representation for raw sensory data is the key stone for various successful MHMS. Conventional methods are the labor-extensive as they usually depend on handcrafted features, which require expert knowledge. Inspired by the success of deep learning methods that redefine representation learning from raw data, we propose local feature-based gated recurrent unit (LFGRU) networks. It is a hybrid approach that combines handcrafted feature design with automatic feature learning for machine health monitoring. First, features from windows of input time series are extracted. Then, an enhanced bidirectional GRU network is designed and applied on the generated sequence of local features to learn the representation. A supervised learning layer is finally trained to predict machine condition. Experiments on three machine health monitoring tasks: tool wear prediction, gearbox fault diagnosis, and incipient bearing fault detection verify the effectiveness and generalization of the proposed LFGRU.
@article{zhao_machine_2018,
title = {Machine {Health} {Monitoring} {Using} {Local} {Feature}-{Based} {Gated} {Recurrent} {Unit} {Networks}},
volume = {65},
issn = {1557-9948},
doi = {10.1109/TIE.2017.2733438},
abstract = {In modern industries, machine health monitoring systems (MHMS) have been applied wildly with the goal of realizing predictive maintenance including failures tracking, downtime reduction, and assets preservation. In the era of big machinery data, data-driven MHMS have achieved remarkable results in the detection of faults after the occurrence of certain failures (diagnosis) and prediction of the future working conditions and the remaining useful life (prognosis). The numerical representation for raw sensory data is the key stone for various successful MHMS. Conventional methods are the labor-extensive as they usually depend on handcrafted features, which require expert knowledge. Inspired by the success of deep learning methods that redefine representation learning from raw data, we propose local feature-based gated recurrent unit (LFGRU) networks. It is a hybrid approach that combines handcrafted feature design with automatic feature learning for machine health monitoring. First, features from windows of input time series are extracted. Then, an enhanced bidirectional GRU network is designed and applied on the generated sequence of local features to learn the representation. A supervised learning layer is finally trained to predict machine condition. Experiments on three machine health monitoring tasks: tool wear prediction, gearbox fault diagnosis, and incipient bearing fault detection verify the effectiveness and generalization of the proposed LFGRU.},
number = {2},
journal = {IEEE Transactions on Industrial Electronics},
author = {Zhao, Rui and Wang, Dongzhe and Yan, Ruqiang and Mao, Kezhi and Shen, Fei and Wang, Jinjiang},
month = feb,
year = {2018},
note = {Conference Name: IEEE Transactions on Industrial Electronics},
keywords = {Computational modeling, Data mining, Fault diagnosis, Feature extraction, Logic gates, Monitoring, Sensors, ecml, feature engineering, feature extraction, feature leanring, gated recurrent unit (GRU), machine health monitoring (MHM), tool wear prediction},
pages = {1539--1548},
}
Downloads: 0
{"_id":"XGrtCEjidXryTe7BX","bibbaseid":"zhao-wang-yan-mao-shen-wang-machinehealthmonitoringusinglocalfeaturebasedgatedrecurrentunitnetworks-2018","author_short":["Zhao, R.","Wang, D.","Yan, R.","Mao, K.","Shen, F.","Wang, J."],"bibdata":{"bibtype":"article","type":"article","title":"Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks","volume":"65","issn":"1557-9948","doi":"10.1109/TIE.2017.2733438","abstract":"In modern industries, machine health monitoring systems (MHMS) have been applied wildly with the goal of realizing predictive maintenance including failures tracking, downtime reduction, and assets preservation. In the era of big machinery data, data-driven MHMS have achieved remarkable results in the detection of faults after the occurrence of certain failures (diagnosis) and prediction of the future working conditions and the remaining useful life (prognosis). The numerical representation for raw sensory data is the key stone for various successful MHMS. Conventional methods are the labor-extensive as they usually depend on handcrafted features, which require expert knowledge. Inspired by the success of deep learning methods that redefine representation learning from raw data, we propose local feature-based gated recurrent unit (LFGRU) networks. It is a hybrid approach that combines handcrafted feature design with automatic feature learning for machine health monitoring. First, features from windows of input time series are extracted. Then, an enhanced bidirectional GRU network is designed and applied on the generated sequence of local features to learn the representation. A supervised learning layer is finally trained to predict machine condition. Experiments on three machine health monitoring tasks: tool wear prediction, gearbox fault diagnosis, and incipient bearing fault detection verify the effectiveness and generalization of the proposed LFGRU.","number":"2","journal":"IEEE Transactions on Industrial Electronics","author":[{"propositions":[],"lastnames":["Zhao"],"firstnames":["Rui"],"suffixes":[]},{"propositions":[],"lastnames":["Wang"],"firstnames":["Dongzhe"],"suffixes":[]},{"propositions":[],"lastnames":["Yan"],"firstnames":["Ruqiang"],"suffixes":[]},{"propositions":[],"lastnames":["Mao"],"firstnames":["Kezhi"],"suffixes":[]},{"propositions":[],"lastnames":["Shen"],"firstnames":["Fei"],"suffixes":[]},{"propositions":[],"lastnames":["Wang"],"firstnames":["Jinjiang"],"suffixes":[]}],"month":"February","year":"2018","note":"Conference Name: IEEE Transactions on Industrial Electronics","keywords":"Computational modeling, Data mining, Fault diagnosis, Feature extraction, Logic gates, Monitoring, Sensors, ecml, feature engineering, feature extraction, feature leanring, gated recurrent unit (GRU), machine health monitoring (MHM), tool wear prediction","pages":"1539–1548","bibtex":"@article{zhao_machine_2018,\n\ttitle = {Machine {Health} {Monitoring} {Using} {Local} {Feature}-{Based} {Gated} {Recurrent} {Unit} {Networks}},\n\tvolume = {65},\n\tissn = {1557-9948},\n\tdoi = {10.1109/TIE.2017.2733438},\n\tabstract = {In modern industries, machine health monitoring systems (MHMS) have been applied wildly with the goal of realizing predictive maintenance including failures tracking, downtime reduction, and assets preservation. In the era of big machinery data, data-driven MHMS have achieved remarkable results in the detection of faults after the occurrence of certain failures (diagnosis) and prediction of the future working conditions and the remaining useful life (prognosis). The numerical representation for raw sensory data is the key stone for various successful MHMS. Conventional methods are the labor-extensive as they usually depend on handcrafted features, which require expert knowledge. Inspired by the success of deep learning methods that redefine representation learning from raw data, we propose local feature-based gated recurrent unit (LFGRU) networks. It is a hybrid approach that combines handcrafted feature design with automatic feature learning for machine health monitoring. First, features from windows of input time series are extracted. Then, an enhanced bidirectional GRU network is designed and applied on the generated sequence of local features to learn the representation. A supervised learning layer is finally trained to predict machine condition. Experiments on three machine health monitoring tasks: tool wear prediction, gearbox fault diagnosis, and incipient bearing fault detection verify the effectiveness and generalization of the proposed LFGRU.},\n\tnumber = {2},\n\tjournal = {IEEE Transactions on Industrial Electronics},\n\tauthor = {Zhao, Rui and Wang, Dongzhe and Yan, Ruqiang and Mao, Kezhi and Shen, Fei and Wang, Jinjiang},\n\tmonth = feb,\n\tyear = {2018},\n\tnote = {Conference Name: IEEE Transactions on Industrial Electronics},\n\tkeywords = {Computational modeling, Data mining, Fault diagnosis, Feature extraction, Logic gates, Monitoring, Sensors, ecml, feature engineering, feature extraction, feature leanring, gated recurrent unit (GRU), machine health monitoring (MHM), tool wear prediction},\n\tpages = {1539--1548},\n}\n\n","author_short":["Zhao, R.","Wang, D.","Yan, R.","Mao, K.","Shen, F.","Wang, J."],"key":"zhao_machine_2018","id":"zhao_machine_2018","bibbaseid":"zhao-wang-yan-mao-shen-wang-machinehealthmonitoringusinglocalfeaturebasedgatedrecurrentunitnetworks-2018","role":"author","urls":{},"keyword":["Computational modeling","Data mining","Fault diagnosis","Feature extraction","Logic gates","Monitoring","Sensors","ecml","feature engineering","feature extraction","feature leanring","gated recurrent unit (GRU)","machine health monitoring (MHM)","tool wear prediction"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["iwKepCrWBps7ojhDx"],"keywords":["computational modeling","data mining","fault diagnosis","feature extraction","logic gates","monitoring","sensors","ecml","feature engineering","feature extraction","feature leanring","gated recurrent unit (gru)","machine health monitoring (mhm)","tool wear prediction"],"search_terms":["machine","health","monitoring","using","local","feature","based","gated","recurrent","unit","networks","zhao","wang","yan","mao","shen","wang"],"title":"Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks","year":2018}