{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T17:03:52Z","timestamp":1771520632832,"version":"3.50.1"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030302405","type":"print"},{"value":"9783030302412","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-30241-2_50","type":"book-chapter","created":{"date-parts":[[2019,8,31]],"date-time":"2019-08-31T09:56:10Z","timestamp":1567245370000},"page":"596-609","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Predicting Air Compressor Failures Using Long Short Term Memory Networks"],"prefix":"10.1007","author":[{"given":"Kunru","family":"Chen","sequence":"first","affiliation":[]},{"given":"Sepideh","family":"Pashami","sequence":"additional","affiliation":[]},{"given":"Yuantao","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Slawomir","family":"Nowaczyk","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,8,30]]},"reference":[{"key":"50_CR1","unstructured":"Prytz, R.: Machine learning methods for vehicle predictive maintenance using off-board and on-board data. Licentiate Thesis (2014)"},{"key":"50_CR2","doi-asserted-by":"crossref","unstructured":"Prytz, R., Nowaczyk, S., Byttner, S.: Towards relation discovery for diagnostics. In: Proceedings of the First International Workshop on Data Mining for Service and Maintenance, KDD4Service 2011, San Diego, California (2011)","DOI":"10.1145\/2018673.2018678"},{"key":"50_CR3","unstructured":"Rognvaldsson, T., Byttner, S., Prytz, R., Nowaczyk, S.: Wisdom of crowds for self-organized intelligent monitoring of vehicle fleets. IEEE Trans. Knowl. Data Eng. (TKDE) (2014)"},{"key":"50_CR4","unstructured":"Prytz, R., Nowaczyk, S., Rognvaldsson, T., Byttner, S.: Analysis of truck compressor failures based on logged vehicle data. In: Proceedings of the 9th International Conference on Data Mining (DMIN 2013), Las Vegas, NV, USA, July 2013"},{"key":"50_CR5","doi-asserted-by":"crossref","unstructured":"Prytz, R., Nowaczyk, S., Rognvaldsson, T., Byttner, S.: Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data. In: Engineering Applications of Artificial Intelligence (2014)","DOI":"10.1016\/j.engappai.2015.02.009"},{"key":"50_CR6","unstructured":"Gugulothu, N., Vishnu, T.V., Malhotra, P., Vig, L., Agarwal, P., Shroff, G.: Predicting remaining useful life using time series embeddings based on recurrent neural networks. arXiv:1709.01073 (2017)"},{"key":"50_CR7","doi-asserted-by":"crossref","unstructured":"Liu, J., Saxena, A., Goebel, K., Saha, B., Wang, W.: An adaptive recurrent neural network for remaining useful life prediction of lithium-ion batteries. National Aeronautics and Space Administration, Moffett Field, CA, AMES Research Center, Technical report (2010)","DOI":"10.36001\/phmconf.2010.v2i1.1896"},{"issue":"2","key":"50_CR8","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1109\/72.279181","volume":"5","author":"Y Bengio","year":"1994","unstructured":"Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157\u2013166 (1994)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"8","key":"50_CR9","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"50_CR10","doi-asserted-by":"crossref","unstructured":"Sak, H., Senior, A., Beaufays, F..: Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: Fifteenth Annual Conference of the International Speech Communication Association (2014)","DOI":"10.21437\/Interspeech.2014-80"},{"key":"50_CR11","unstructured":"Graves, A., Schmidhuber, J.: Offline handwriting recognition with multidimensional recurrent neural networks. In: Advances in Neural Information Processing Systems, pp. 545\u2013552 (2009)"},{"key":"50_CR12","doi-asserted-by":"crossref","unstructured":"Zheng, S., Ristovski, K., Farahat, A., Gupta, C.: Long short-term memory network for remaining useful life estimation. In: 2017 IEEE International Conference on Prognostics and Health Management (ICPHM), pp. 88\u201395 (2017)","DOI":"10.1109\/ICPHM.2017.7998311"},{"key":"50_CR13","doi-asserted-by":"crossref","unstructured":"Zhao, G., Zhang, G., Liu, Y., Zhang, B., Hu, C.: Lithiumion battery remaining useful life prediction with deep belief network and relevance vector machine. In: 2017 IEEE International Conference on Prognostics and Health Management (ICPHM), pp. 7\u201313. IEEE (2017)","DOI":"10.1109\/ICPHM.2017.7998298"},{"key":"50_CR14","unstructured":"Chen, K., Fan, Y., Pashami, S., Nowaczyk, S.: Recurrent neural networks for fault detection. Master Thesis, Halmstad (2018)"},{"key":"50_CR15","unstructured":"Ho, T.: Random decision forests. In: Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, 14\u201316 August 1995"},{"key":"50_CR16","unstructured":"Schwabacher, M., Goebel, K.: A survey of artificial intelligence for prognostics. In: AAAI Fall Symposium, pp. 107\u2013114 (2007)"},{"issue":"3","key":"50_CR17","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.1109\/TPWRD.2013.2248068","volume":"28","author":"A Ahmadimanesh","year":"2013","unstructured":"Ahmadimanesh, A., Shahrtash, S.: Transient-based fault-location method for multiterminal lines employing s-transform. IEEE Trans. Power Delivery 28(3), 1373\u20131380 (2013)","journal-title":"IEEE Trans. Power Delivery"},{"issue":"5","key":"50_CR18","doi-asserted-by":"publisher","first-page":"055604","DOI":"10.1088\/0957-0233\/23\/5\/055604","volume":"23","author":"Jie Liu","year":"2012","unstructured":"Liu, J.: Shannon wavelet spectrum analysis on truncated vibration signals for machine incipient fault detection. Measur. Sci. Technol. 23(5) (2012). https:\/\/doi.org\/10.1088\/0957-0233\/23\/5\/055604","journal-title":"Measurement Science and Technology"},{"issue":"5","key":"50_CR19","doi-asserted-by":"publisher","first-page":"1051","DOI":"10.1109\/TFUZZ.2016.2593921","volume":"25","author":"A Chibani","year":"2017","unstructured":"Chibani, A., Chadli, M., Shi, P., Braiek, N.: Fuzzy fault detection filter design for t-s fuzzy systems in the finite-frequency domain. IEEE Trans. Fuzzy Syst. 25(5), 1051\u20131061 (2017)","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"6","key":"50_CR20","doi-asserted-by":"publisher","first-page":"2607","DOI":"10.1016\/j.ymssp.2006.12.004","volume":"21","author":"VK Rai","year":"2007","unstructured":"Rai, V.K., Mohanty, A.R.: Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform. Mech. Syst. Signal Process. 21(6), 2607\u20132615 (2007)","journal-title":"Mech. Syst. Signal Process."},{"issue":"4","key":"50_CR21","doi-asserted-by":"publisher","first-page":"1305","DOI":"10.1007\/s00034-014-9894-2","volume":"34","author":"G Zhong","year":"2015","unstructured":"Zhong, G., Yang, G.: Fault detection for discrete-time switched systems in finite-frequency domain. Circuits Syst. Signal Process. 34(4), 1305\u20131324 (2015)","journal-title":"Circuits Syst. Signal Process."},{"key":"50_CR22","doi-asserted-by":"crossref","unstructured":"Heimes, F.: Recurrent neural networks for remaining useful life estimation. In: International Conference on Prognostics and Health Management, pp. 1\u20136. IEEE (2008)","DOI":"10.1109\/PHM.2008.4711422"},{"issue":"3","key":"50_CR23","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1109\/TIM.2010.2078296","volume":"60","author":"A Malhi","year":"2011","unstructured":"Malhi, A., Yan, R., Gao, R.: Prognosis of defect propagation based on recurrent neural networks. IEEE Trans. Instrum. Meas. 60(3), 703\u2013711 (2011)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"50_CR24","doi-asserted-by":"crossref","unstructured":"Rigamonti, M., Baraldi, P., Zio, E.: Echo state network for the remaining useful life prediction of a turbofan engine. In: Third European Conference of the Prognostics and Health Management Society (2016)","DOI":"10.36001\/phme.2016.v3i1.1623"},{"key":"50_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1007\/978-3-319-32025-0_14","volume-title":"Database Systems for Advanced Applications","author":"G Sateesh Babu","year":"2016","unstructured":"Sateesh Babu, G., Zhao, P., Li, X.-L.: Deep convolutional neural network based regression approach for estimation of remaining useful life. In: Navathe, S.B., Wu, W., Shekhar, S., Du, X., Wang, X.S., Xiong, H. (eds.) DASFAA 2016. LNCS, vol. 9642, pp. 214\u2013228. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-32025-0_14"},{"key":"50_CR26","doi-asserted-by":"crossref","unstructured":"Saxena, A., Goebel, K., Simon, D., Eklund, N.: Damage propagation modeling for aircraft engine run-to-failure simulation. In: International Conference on the Prognostics and Health Management, pp. 1\u20139. IEEE (2008)","DOI":"10.1109\/PHM.2008.4711414"}],"container-title":["Lecture Notes in Computer Science","Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30241-2_50","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,22]],"date-time":"2024-07-22T20:44:59Z","timestamp":1721681099000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-30241-2_50"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030302405","9783030302412"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30241-2_50","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"30 August 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"EPIA Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vila Real","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"epia2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/epia2019.utad.pt\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"252","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"119","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"47% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.32","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1.86","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}