@inproceedings{zhao_machine_2016,
	title = {Machine health monitoring with {LSTM} networks},
	doi = {10.1109/ICSensT.2016.7796266},
	abstract = {Effective machine health monitoring systems are critical to modern manufacturing systems and industries. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, sensory data that is a kind of sequential data can not serve as direct meaningful representations for machine conditions due to its noise, varying length and irregular sampling. A majority of previous models focus on feature extraction/fusion methods that involve expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, representation learning from raw data has been redefined. Among deep learning models, Long Short-Term Memory networks (LSTMs) are able to capture long-term dependencies and model sequential data. Therefore, LSTMs is able to work on the sensory data of machine condition. Here, the first study about a empirical evaluation of LSTMs-based machine health monitoring systems is presented. A real life tool wear test is introduced. Basic and deep LSTMs are designed to predict the actual tool wear based on raw sensory data. The experimental results have shown that our models, especially deep LSTMs, are able to outperform several state-of-arts baseline methods.},
	booktitle = {2016 10th {International} {Conference} on {Sensing} {Technology} ({ICST})},
	author = {Zhao, Rui and Wang, Jinjiang and Yan, Ruqiang and Mao, Kezhi},
	month = nov,
	year = {2016},
	note = {ISSN: 2156-8073},
	keywords = {Data models, Feature extraction, LSTMs, Machine Health Monitoring, Mathematical model, Monitoring, Neural networks, RNN, Sensors, Tool Wear Prediction, Training, ecml, feature extraction, feature learning},
	pages = {1--6},
}

{"_id":"49BAbBKxydodRvYc7","bibbaseid":"zhao-wang-yan-mao-machinehealthmonitoringwithlstmnetworks-2016","author_short":["Zhao, R.","Wang, J.","Yan, R.","Mao, K."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","title":"Machine health monitoring with LSTM networks","doi":"10.1109/ICSensT.2016.7796266","abstract":"Effective machine health monitoring systems are critical to modern manufacturing systems and industries. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, sensory data that is a kind of sequential data can not serve as direct meaningful representations for machine conditions due to its noise, varying length and irregular sampling. A majority of previous models focus on feature extraction/fusion methods that involve expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, representation learning from raw data has been redefined. Among deep learning models, Long Short-Term Memory networks (LSTMs) are able to capture long-term dependencies and model sequential data. Therefore, LSTMs is able to work on the sensory data of machine condition. Here, the first study about a empirical evaluation of LSTMs-based machine health monitoring systems is presented. A real life tool wear test is introduced. Basic and deep LSTMs are designed to predict the actual tool wear based on raw sensory data. The experimental results have shown that our models, especially deep LSTMs, are able to outperform several state-of-arts baseline methods.","booktitle":"2016 10th International Conference on Sensing Technology (ICST)","author":[{"propositions":[],"lastnames":["Zhao"],"firstnames":["Rui"],"suffixes":[]},{"propositions":[],"lastnames":["Wang"],"firstnames":["Jinjiang"],"suffixes":[]},{"propositions":[],"lastnames":["Yan"],"firstnames":["Ruqiang"],"suffixes":[]},{"propositions":[],"lastnames":["Mao"],"firstnames":["Kezhi"],"suffixes":[]}],"month":"November","year":"2016","note":"ISSN: 2156-8073","keywords":"Data models, Feature extraction, LSTMs, Machine Health Monitoring, Mathematical model, Monitoring, Neural networks, RNN, Sensors, Tool Wear Prediction, Training, ecml, feature extraction, feature learning","pages":"1–6","bibtex":"@inproceedings{zhao_machine_2016,\n\ttitle = {Machine health monitoring with {LSTM} networks},\n\tdoi = {10.1109/ICSensT.2016.7796266},\n\tabstract = {Effective machine health monitoring systems are critical to modern manufacturing systems and industries. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, sensory data that is a kind of sequential data can not serve as direct meaningful representations for machine conditions due to its noise, varying length and irregular sampling. A majority of previous models focus on feature extraction/fusion methods that involve expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, representation learning from raw data has been redefined. Among deep learning models, Long Short-Term Memory networks (LSTMs) are able to capture long-term dependencies and model sequential data. Therefore, LSTMs is able to work on the sensory data of machine condition. Here, the first study about a empirical evaluation of LSTMs-based machine health monitoring systems is presented. A real life tool wear test is introduced. Basic and deep LSTMs are designed to predict the actual tool wear based on raw sensory data. The experimental results have shown that our models, especially deep LSTMs, are able to outperform several state-of-arts baseline methods.},\n\tbooktitle = {2016 10th {International} {Conference} on {Sensing} {Technology} ({ICST})},\n\tauthor = {Zhao, Rui and Wang, Jinjiang and Yan, Ruqiang and Mao, Kezhi},\n\tmonth = nov,\n\tyear = {2016},\n\tnote = {ISSN: 2156-8073},\n\tkeywords = {Data models, Feature extraction, LSTMs, Machine Health Monitoring, Mathematical model, Monitoring, Neural networks, RNN, Sensors, Tool Wear Prediction, Training, ecml, feature extraction, feature learning},\n\tpages = {1--6},\n}\n\n","author_short":["Zhao, R.","Wang, J.","Yan, R.","Mao, K."],"key":"zhao_machine_2016","id":"zhao_machine_2016","bibbaseid":"zhao-wang-yan-mao-machinehealthmonitoringwithlstmnetworks-2016","role":"author","urls":{},"keyword":["Data models","Feature extraction","LSTMs","Machine Health Monitoring","Mathematical model","Monitoring","Neural networks","RNN","Sensors","Tool Wear Prediction","Training","ecml","feature extraction","feature learning"],"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["iwKepCrWBps7ojhDx"],"keywords":["data models","feature extraction","lstms","machine health monitoring","mathematical model","monitoring","neural networks","rnn","sensors","tool wear prediction","training","ecml","feature extraction","feature learning"],"search_terms":["machine","health","monitoring","lstm","networks","zhao","wang","yan","mao"],"title":"Machine health monitoring with LSTM networks","year":2016}