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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
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2018 (English)In: International Journal of Prognostics and Health Management, ISSN 2153-2648, E-ISSN 2153-2648, Vol. 9, no 1, article id 007Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
2018. Vol. 9, no 1, article id 007
National Category
Mechanical Engineering
Research subject
Technology (byts ev till Engineering), Mechanical Engineering
Identifiers
URN: urn:nbn:se:lnu:diva-73278OAI: oai:DiVA.org:lnu-73278DiVA, id: diva2:1199885
Available from: 2018-04-23 Created: 2018-04-23 Last updated: 2018-10-17Bibliographically approved

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Håkansson, Lars

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
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  • vancouver
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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