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dc.contributor.authorChalouli, Mohammed
dc.contributor.authorBerrached, Nasr-eddine
dc.contributor.authorDenai, Mouloud
dc.date.accessioned2017-11-07T16:45:54Z
dc.date.available2017-11-07T16:45:54Z
dc.date.issued2017-10-01
dc.identifier.citationChalouli , M , Berrached , N & Denai , M 2017 , ' Intelligent Health Monitoring of Machine Bearings Based on Feature Extraction ' , Journal of Failure Analysis and Prevention , vol. 17 , no. 5 , pp. 1053-1066 . https://doi.org/10.1007/s11668-017-0343-y
dc.identifier.issn1547-7029
dc.identifier.urihttp://hdl.handle.net/2299/19506
dc.descriptionThis document is the Accepted Manuscript of the following article: Mohammed Chalouli, Nasr-eddine Berrached, and Mouloud Denai, ‘Intelligent Health Monitoring of Machine Bearings Based on Feature Extraction’, Journal of Failure Analysis and Prevention, Vol. 17 (5): 1053-1066, October 2017. Under embargo. Embargo end date: 31 August 2018. The final publication is available at Springer via DOI: https://doi.org/10.1007/s11668-017-0343-y.
dc.description.abstractFinding reliable condition monitoring solutions for large-scale complex systems is currently a major challenge in industrial research. Since fault diagnosis is directly related to the features of a system, there have been many research studies aimed to develop methods for the selection of the relevant features. Moreover, there are no universal features for a particular application domain such as machine diagnosis. For example, in machine bearing fault diagnosis, these features are often selected by an expert or based on previous experience. Thus, for each bearing machine type, the relevant features must be selected. This paper attempts to solve the problem of relevant features identification by building an automatic fault diagnosis process based on relevant feature selection using a data-driven approach. The proposed approach starts with the extraction of the time-domain features from the input signals. Then, a feature reduction algorithm based on cross-correlation filter is applied to reduce the time and cost of the processing. Unsupervised learning mechanism using K-means++ selects the relevant fault features based on the squared Euclidian distance between different health states. Finally, the selected features are used as inputs to a self-organizing map producing our health indicator. The proposed method is tested on roller bearing benchmark datasets.en
dc.format.extent16
dc.format.extent1624939
dc.language.isoeng
dc.relation.ispartofJournal of Failure Analysis and Prevention
dc.subjectFailure diagnosis; Bearing faults; Time-domain features; Condition-based maintenance; Health indicators; Relevant features; Fault feature extraction
dc.titleIntelligent Health Monitoring of Machine Bearings Based on Feature Extractionen
dc.contributor.institutionSchool of Engineering and Technology
dc.contributor.institutionCentre for Engineering Research
dc.contributor.institutionSmart Electronics Devices and Networks
dc.contributor.institutionCommunications and Intelligent Systems
dc.description.statusPeer reviewed
dc.date.embargoedUntil2018-08-31
rioxxterms.versionofrecord10.1007/s11668-017-0343-y
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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