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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-23821
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dc.contributor.authorFathi, Kiavash-
dc.contributor.authorvan de Venn, Hans Wernher-
dc.contributor.authorHonegger, Marcel-
dc.date.accessioned2021-12-22T14:09:35Z-
dc.date.available2021-12-22T14:09:35Z-
dc.date.issued2021-10-21-
dc.identifier.issn1424-8220de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/23821-
dc.description.abstractPerforming predictive maintenance (PdM) is challenging for many reasons. Dealing with large datasets which may not contain run-to-failure data (R2F) complicates PdM even more. When no R2F data are available, identifying condition indicators (CIs), estimating the health index (HI), and thereafter, calculating a degradation model for predicting the remaining useful lifetime (RUL) are merely impossible using supervised learning. In this paper, a 3 DoF delta robot used for pick and place task is studied. In the proposed method, autoencoders (AEs) are used to predict when maintenance is required based on the signal sequence distribution and anomaly detection, which is vital when no R2F data are available. Due to the sequential nature of the data, nonlinearity of the system, and correlations between parameter time-series, convolutional layers are used for feature extraction. Thereafter, a sigmoid function is used to predict the probability of having an anomaly given CIs acquired from AEs. This function can be manually tuned given the sensitivity of the system or optimized by solving a minimax problem. Moreover, the proposed architecture can be used for fault localization for the specified system. Additionally, the proposed method can calculate RUL using Gaussian process (GP), as a degradation model, given HI values as its input.de_CH
dc.language.isoende_CH
dc.publisherMDPIde_CH
dc.relation.ispartofSensorsde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectAnomaly detectionde_CH
dc.subjectAutoencoderde_CH
dc.subjectData-driven maintenancede_CH
dc.subjectDeep learningde_CH
dc.subjectGaussian processesde_CH
dc.subjectPredictive maintenancede_CH
dc.subjectNormal distributionde_CH
dc.subjectProbabilityde_CH
dc.subjectRoboticsde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc621.3: Elektro-, Kommunikations-, Steuerungs- und Regelungstechnikde_CH
dc.titlePredictive maintenance : an autoencoder anomaly-based approach for a 3 DoF delta robotde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Mechatronische Systeme (IMS)de_CH
dc.identifier.doi10.3390/s21216979de_CH
dc.identifier.doi10.21256/zhaw-23821-
dc.identifier.pmid34770289de_CH
zhaw.funding.euNode_CH
zhaw.issue21de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.start6979de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume21de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedIndustrie 4.0de_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
zhaw.monitoring.costperiod2021de_CH
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Fathi, K., van de Venn, H. W., & Honegger, M. (2021). Predictive maintenance : an autoencoder anomaly-based approach for a 3 DoF delta robot. Sensors, 21(21), 6979. https://doi.org/10.3390/s21216979
Fathi, K., van de Venn, H.W. and Honegger, M. (2021) ‘Predictive maintenance : an autoencoder anomaly-based approach for a 3 DoF delta robot’, Sensors, 21(21), p. 6979. Available at: https://doi.org/10.3390/s21216979.
K. Fathi, H. W. van de Venn, and M. Honegger, “Predictive maintenance : an autoencoder anomaly-based approach for a 3 DoF delta robot,” Sensors, vol. 21, no. 21, p. 6979, Oct. 2021, doi: 10.3390/s21216979.
FATHI, Kiavash, Hans Wernher VAN DE VENN und Marcel HONEGGER, 2021. Predictive maintenance : an autoencoder anomaly-based approach for a 3 DoF delta robot. Sensors. 21 Oktober 2021. Bd. 21, Nr. 21, S. 6979. DOI 10.3390/s21216979
Fathi, Kiavash, Hans Wernher van de Venn, and Marcel Honegger. 2021. “Predictive Maintenance : An Autoencoder Anomaly-Based Approach for a 3 DoF Delta Robot.” Sensors 21 (21): 6979. https://doi.org/10.3390/s21216979.
Fathi, Kiavash, et al. “Predictive Maintenance : An Autoencoder Anomaly-Based Approach for a 3 DoF Delta Robot.” Sensors, vol. 21, no. 21, Oct. 2021, p. 6979, https://doi.org/10.3390/s21216979.


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