A Fault Detection Framework Based on LSTM Autoencoder: A Case Study for Volvo Bus Data Set. Davari, N., Pashami, S., Veloso, B., Nowaczyk, S., Fan, Y., Pereira, P. M., Ribeiro, R. P., & Gama, J. In Bouadi, T., Fromont, E., & Hüllermeier, E., editors, Advances in Intelligent Data Analysis XX, of Lecture Notes in Computer Science, pages 39–52, Cham, 2022. Springer International Publishing. doi abstract bibtex This study applies a data-driven anomaly detection framework based on a Long Short-Term Memory (LSTM) autoencoder network for several subsystems of a public transport bus. The proposed framework efficiently detects abnormal data, significantly reducing the false alarm rate compared to available alternatives. Using historical repair records, we demonstrate how detection of abnormal sequences in the signals can be used for predicting equipment failures. The deviations from normal operation patterns are detected by analysing the data collected from several on-board sensors (e.g., wet tank air pressure, engine speed, engine load) installed on the bus. The performance of LSTM autoencoder (LSTM-AE) is compared against the multi-layer autoencoder (mlAE) network in the same anomaly detection framework. The experimental results show that the performance indicators of the LSTM-AE network, in terms of F1 Score, Recall, and Precision, are better than those of the mlAE network.
@inproceedings{davari_fault_2022,
address = {Cham},
series = {Lecture {Notes} in {Computer} {Science}},
title = {A {Fault} {Detection} {Framework} {Based} on {LSTM} {Autoencoder}: {A} {Case} {Study} for {Volvo} {Bus} {Data} {Set}},
isbn = {978-3-031-01333-1},
shorttitle = {A {Fault} {Detection} {Framework} {Based} on {LSTM} {Autoencoder}},
doi = {10.1007/978-3-031-01333-1_4},
abstract = {This study applies a data-driven anomaly detection framework based on a Long Short-Term Memory (LSTM) autoencoder network for several subsystems of a public transport bus. The proposed framework efficiently detects abnormal data, significantly reducing the false alarm rate compared to available alternatives. Using historical repair records, we demonstrate how detection of abnormal sequences in the signals can be used for predicting equipment failures. The deviations from normal operation patterns are detected by analysing the data collected from several on-board sensors (e.g., wet tank air pressure, engine speed, engine load) installed on the bus. The performance of LSTM autoencoder (LSTM-AE) is compared against the multi-layer autoencoder (mlAE) network in the same anomaly detection framework. The experimental results show that the performance indicators of the LSTM-AE network, in terms of F1 Score, Recall, and Precision, are better than those of the mlAE network.},
language = {en},
booktitle = {Advances in {Intelligent} {Data} {Analysis} {XX}},
publisher = {Springer International Publishing},
author = {Davari, Narjes and Pashami, Sepideh and Veloso, Bruno and Nowaczyk, Sławomir and Fan, Yuantao and Pereira, Pedro Mota and Ribeiro, Rita P. and Gama, João},
editor = {Bouadi, Tassadit and Fromont, Elisa and Hüllermeier, Eyke},
year = {2022},
keywords = {Autoencoder, Fault detection, LSTM, Outliers, Time series},
pages = {39--52},
}
Downloads: 0
{"_id":"6j8PJgoD3vBHviruK","bibbaseid":"davari-pashami-veloso-nowaczyk-fan-pereira-ribeiro-gama-afaultdetectionframeworkbasedonlstmautoencoderacasestudyforvolvobusdataset-2022","author_short":["Davari, N.","Pashami, S.","Veloso, B.","Nowaczyk, S.","Fan, Y.","Pereira, P. M.","Ribeiro, R. P.","Gama, J."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","address":"Cham","series":"Lecture Notes in Computer Science","title":"A Fault Detection Framework Based on LSTM Autoencoder: A Case Study for Volvo Bus Data Set","isbn":"978-3-031-01333-1","shorttitle":"A Fault Detection Framework Based on LSTM Autoencoder","doi":"10.1007/978-3-031-01333-1_4","abstract":"This study applies a data-driven anomaly detection framework based on a Long Short-Term Memory (LSTM) autoencoder network for several subsystems of a public transport bus. The proposed framework efficiently detects abnormal data, significantly reducing the false alarm rate compared to available alternatives. Using historical repair records, we demonstrate how detection of abnormal sequences in the signals can be used for predicting equipment failures. The deviations from normal operation patterns are detected by analysing the data collected from several on-board sensors (e.g., wet tank air pressure, engine speed, engine load) installed on the bus. The performance of LSTM autoencoder (LSTM-AE) is compared against the multi-layer autoencoder (mlAE) network in the same anomaly detection framework. The experimental results show that the performance indicators of the LSTM-AE network, in terms of F1 Score, Recall, and Precision, are better than those of the mlAE network.","language":"en","booktitle":"Advances in Intelligent Data Analysis XX","publisher":"Springer International Publishing","author":[{"propositions":[],"lastnames":["Davari"],"firstnames":["Narjes"],"suffixes":[]},{"propositions":[],"lastnames":["Pashami"],"firstnames":["Sepideh"],"suffixes":[]},{"propositions":[],"lastnames":["Veloso"],"firstnames":["Bruno"],"suffixes":[]},{"propositions":[],"lastnames":["Nowaczyk"],"firstnames":["Sławomir"],"suffixes":[]},{"propositions":[],"lastnames":["Fan"],"firstnames":["Yuantao"],"suffixes":[]},{"propositions":[],"lastnames":["Pereira"],"firstnames":["Pedro","Mota"],"suffixes":[]},{"propositions":[],"lastnames":["Ribeiro"],"firstnames":["Rita","P."],"suffixes":[]},{"propositions":[],"lastnames":["Gama"],"firstnames":["João"],"suffixes":[]}],"editor":[{"propositions":[],"lastnames":["Bouadi"],"firstnames":["Tassadit"],"suffixes":[]},{"propositions":[],"lastnames":["Fromont"],"firstnames":["Elisa"],"suffixes":[]},{"propositions":[],"lastnames":["Hüllermeier"],"firstnames":["Eyke"],"suffixes":[]}],"year":"2022","keywords":"Autoencoder, Fault detection, LSTM, Outliers, Time series","pages":"39–52","bibtex":"@inproceedings{davari_fault_2022,\n\taddress = {Cham},\n\tseries = {Lecture {Notes} in {Computer} {Science}},\n\ttitle = {A {Fault} {Detection} {Framework} {Based} on {LSTM} {Autoencoder}: {A} {Case} {Study} for {Volvo} {Bus} {Data} {Set}},\n\tisbn = {978-3-031-01333-1},\n\tshorttitle = {A {Fault} {Detection} {Framework} {Based} on {LSTM} {Autoencoder}},\n\tdoi = {10.1007/978-3-031-01333-1_4},\n\tabstract = {This study applies a data-driven anomaly detection framework based on a Long Short-Term Memory (LSTM) autoencoder network for several subsystems of a public transport bus. The proposed framework efficiently detects abnormal data, significantly reducing the false alarm rate compared to available alternatives. Using historical repair records, we demonstrate how detection of abnormal sequences in the signals can be used for predicting equipment failures. The deviations from normal operation patterns are detected by analysing the data collected from several on-board sensors (e.g., wet tank air pressure, engine speed, engine load) installed on the bus. The performance of LSTM autoencoder (LSTM-AE) is compared against the multi-layer autoencoder (mlAE) network in the same anomaly detection framework. The experimental results show that the performance indicators of the LSTM-AE network, in terms of F1 Score, Recall, and Precision, are better than those of the mlAE network.},\n\tlanguage = {en},\n\tbooktitle = {Advances in {Intelligent} {Data} {Analysis} {XX}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Davari, Narjes and Pashami, Sepideh and Veloso, Bruno and Nowaczyk, Sławomir and Fan, Yuantao and Pereira, Pedro Mota and Ribeiro, Rita P. and Gama, João},\n\teditor = {Bouadi, Tassadit and Fromont, Elisa and Hüllermeier, Eyke},\n\tyear = {2022},\n\tkeywords = {Autoencoder, Fault detection, LSTM, Outliers, Time series},\n\tpages = {39--52},\n}\n\n","author_short":["Davari, N.","Pashami, S.","Veloso, B.","Nowaczyk, S.","Fan, Y.","Pereira, P. M.","Ribeiro, R. P.","Gama, J."],"editor_short":["Bouadi, T.","Fromont, E.","Hüllermeier, E."],"key":"davari_fault_2022","id":"davari_fault_2022","bibbaseid":"davari-pashami-veloso-nowaczyk-fan-pereira-ribeiro-gama-afaultdetectionframeworkbasedonlstmautoencoderacasestudyforvolvobusdataset-2022","role":"author","urls":{},"keyword":["Autoencoder","Fault detection","LSTM","Outliers","Time series"],"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["iwKepCrWBps7ojhDx"],"keywords":["autoencoder","fault detection","lstm","outliers","time series"],"search_terms":["fault","detection","framework","based","lstm","autoencoder","case","study","volvo","bus","data","set","davari","pashami","veloso","nowaczyk","fan","pereira","ribeiro","gama"],"title":"A Fault Detection Framework Based on LSTM Autoencoder: A Case Study for Volvo Bus Data Set","year":2022}