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On-board Clutch Slippage Detection and Diagnosis in Heavy Duty Machine
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development. Volvo Construction Equipment, Eskilstuna, Sweden. (The Faste Laboratory, A VINNOVA Excellence Centre for Functional Product Innovation)
SICS Swedish ICT, Kista, Sweden.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. (The Faste Laboratory, A VINNOVA Excellence Centre for Functional Product Innovation)
Linnaeus University, Växjö, Sweden.
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2017 (English)In: International Journal of Prognostics and Health Management, ISSN ISSN2153-2648Article in journal (Refereed) Submitted
Abstract [en]

In order to reduce unnecessary stops and expensive downtime originating from clutch failure of construction equipment machines; adequate real time sensor data measured on the machinein combination with feature extraction and classification methods may be utilized.This paper, based on a study at Volvo Construction Equipment,presents a framework with feature extraction methods and an anomaly detection module combined with Case-Based Reasoning (CBR) for on-board clutch slippage detection and diagnosis in a 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 anomalyis detected, the Case-Based diagnosis module is activated for fault severity estimation.

Place, publisher, year, edition, pages
National Category
Engineering and Technology
Research subject
Machine Learning; Signal Processing
URN: urn:nbn:se:ltu:diva-61322OAI: diva2:1062850
Available from: 2017-01-09 Created: 2017-01-09 Last updated: 2017-01-11

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Källström, Elisabeth
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