@inproceedings{maschler_continual_2020,
	title = {Continual {Learning} of {Fault} {Prediction} for {Turbofan} {Engines} using {Deep} {Learning} with {Elastic} {Weight} {Consolidation}},
	volume = {1},
	doi = {10.1109/ETFA46521.2020.9211903},
	abstract = {Fault prediction based upon deep learning algorithms has great potential in industrial automation: By automatically adapting to different usage contexts, it would greatly expand the usefulness of current predictive maintenance solutions. However, restrictions regarding the centralized accumulation of data necessary for such automatic adaption call for a distributed approach to training these algorithms. Therefore, in this paper, a continual learning based algorithm for fault prediction is presented, allowing for distributed, cooperative learning by elastic weight consolidation. This algorithm is then evaluated on a large NASA turbofan engine dataset and shows promising results regarding the performant training on decentral sub-datasets for industrial automation scenarios.},
	booktitle = {2020 25th {IEEE} {International} {Conference} on {Emerging} {Technologies} and {Factory} {Automation} ({ETFA})},
	author = {Maschler, Benjamin and Vietz, Hannes and Jazdi, Nasser and Weyrich, Michael},
	month = sep,
	year = {2020},
	note = {ISSN: 1946-0759},
	keywords = {Continual learning, Deep learning, Elastic weight consolidation, Engines, Fault prognostics, Industrial Automation, Machine learning, NASA, Neural networks, Prediction algorithms, Prediction methods, Task analysis, Training, Training data, Transfer learning},
	pages = {959--966},
}

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Therefore, in this paper, a continual learning based algorithm for fault prediction is presented, allowing for distributed, cooperative learning by elastic weight consolidation. This algorithm is then evaluated on a large NASA turbofan engine dataset and shows promising results regarding the performant training on decentral sub-datasets for industrial automation scenarios.","booktitle":"2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)","author":[{"propositions":[],"lastnames":["Maschler"],"firstnames":["Benjamin"],"suffixes":[]},{"propositions":[],"lastnames":["Vietz"],"firstnames":["Hannes"],"suffixes":[]},{"propositions":[],"lastnames":["Jazdi"],"firstnames":["Nasser"],"suffixes":[]},{"propositions":[],"lastnames":["Weyrich"],"firstnames":["Michael"],"suffixes":[]}],"month":"September","year":"2020","note":"ISSN: 1946-0759","keywords":"Continual learning, Deep learning, Elastic weight consolidation, Engines, Fault prognostics, Industrial Automation, Machine learning, NASA, Neural networks, Prediction algorithms, Prediction methods, Task analysis, Training, Training data, Transfer learning","pages":"959–966","bibtex":"@inproceedings{maschler_continual_2020,\n\ttitle = {Continual {Learning} of {Fault} {Prediction} for {Turbofan} {Engines} using {Deep} {Learning} with {Elastic} {Weight} {Consolidation}},\n\tvolume = {1},\n\tdoi = {10.1109/ETFA46521.2020.9211903},\n\tabstract = {Fault prediction based upon deep learning algorithms has great potential in industrial automation: By automatically adapting to different usage contexts, it would greatly expand the usefulness of current predictive maintenance solutions. However, restrictions regarding the centralized accumulation of data necessary for such automatic adaption call for a distributed approach to training these algorithms. Therefore, in this paper, a continual learning based algorithm for fault prediction is presented, allowing for distributed, cooperative learning by elastic weight consolidation. This algorithm is then evaluated on a large NASA turbofan engine dataset and shows promising results regarding the performant training on decentral sub-datasets for industrial automation scenarios.},\n\tbooktitle = {2020 25th {IEEE} {International} {Conference} on {Emerging} {Technologies} and {Factory} {Automation} ({ETFA})},\n\tauthor = {Maschler, Benjamin and Vietz, Hannes and Jazdi, Nasser and Weyrich, Michael},\n\tmonth = sep,\n\tyear = {2020},\n\tnote = {ISSN: 1946-0759},\n\tkeywords = {Continual learning, Deep learning, Elastic weight consolidation, Engines, Fault prognostics, Industrial Automation, Machine learning, NASA, Neural networks, Prediction algorithms, Prediction methods, Task analysis, Training, Training data, Transfer learning},\n\tpages = {959--966},\n}\n\n\n\n\n\n\n\n","author_short":["Maschler, B.","Vietz, H.","Jazdi, N.","Weyrich, M."],"key":"maschler_continual_2020","id":"maschler_continual_2020","bibbaseid":"maschler-vietz-jazdi-weyrich-continuallearningoffaultpredictionforturbofanenginesusingdeeplearningwithelasticweightconsolidation-2020","role":"author","urls":{},"keyword":["Continual learning","Deep learning","Elastic weight consolidation","Engines","Fault prognostics","Industrial Automation","Machine learning","NASA","Neural networks","Prediction algorithms","Prediction methods","Task analysis","Training","Training data","Transfer learning"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"inproceedings","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["iwKepCrWBps7ojhDx"],"keywords":["continual learning","deep learning","elastic weight consolidation","engines","fault prognostics","industrial automation","machine learning","nasa","neural networks","prediction algorithms","prediction methods","task analysis","training","training data","transfer learning"],"search_terms":["continual","learning","fault","prediction","turbofan","engines","using","deep","learning","elastic","weight","consolidation","maschler","vietz","jazdi","weyrich"],"title":"Continual Learning of Fault Prediction for Turbofan Engines using Deep Learning with Elastic Weight Consolidation","year":2020}