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On-board Clutch Slippage Detection and Diagnosis in Heavy Duty Machine
Lulea University of Technology ; Volvo Construction Equipment.
RISE SICS .
Lulea University of Technology.
Blekinge Institute of Technology. (Mechanical Engineering)ORCID-id: 0000-0001-7732-1898
Vise andre og tillknytning
2018 (engelsk)Inngår i: International Journal of Prognostics and Health Management, ISSN 2153-2648, E-ISSN 2153-2648, Vol. 9, nr 1, artikkel-id 007Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

In order to reduce unnecessary stops and expensive downtime originating from clutchfailure of construction equipment machines; adequate real time sensor data measured on the machine in combination with feature extraction and classification methods may be utilized.

This paper presents a framework with feature extraction methods and an anomalydetection module combined with Case-Based Reasoning (CBR) for on-board clutch slippagedetection and diagnosis in heavy duty equipment. The feature extraction methods used are Moving Average Square Value Filtering (MASVF) and a measure of the fourth order statistical properties of the signals implemented as continuous queries over data streams.The anomaly detection module has two components, the Gaussian Mixture Model(GMM) and the Logistics Regression classifier.CBR is a learning approach that classifies faults by creating a new solution for a new fault case from the solution of the previous fault cases.Through use of a data stream management system and continuous queries (CQs), the anomaly detection module continuously waits for a clutch slippage event detected by the feature extraction methods, the query returns a set of features, which activates the anomaly detection module. The first component of the anomaly detection module trains a GMM to extracted features while the second component uses a Logistic Regression classifier for classifying normal and anomalous data. When an anomaly is detected, the Case-Based diagnosis module is activated for fault severity estimation.

sted, utgiver, år, opplag, sider
2018. Vol. 9, nr 1, artikkel-id 007
HSV kategori
Forskningsprogram
Teknik, Maskinteknik
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URN: urn:nbn:se:lnu:diva-73278OAI: oai:DiVA.org:lnu-73278DiVA, id: diva2:1199885
Tilgjengelig fra: 2018-04-23 Laget: 2018-04-23 Sist oppdatert: 2018-10-17bibliografisk kontrollert

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