On-board Clutch Slippage Detection and Diagnosis in Heavy Duty Machine

Elisabeth Källström, Tomas Olsson, John Lindström, Lars Håkansson, and Jonas Larsson
Publication Target: 
IJPHM
Publication Issue: 
1
Submission Type: 
Full Paper
Supporting Agencies (optional): 
SMART VORTEX (Scalable Semantic Product Data Stream Management for Collaboration and Decision Making in Engineering), FP7 EU Large scale Integrating Project and Volvo CE.
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ijphm_18_007.pdf539.37 KBMarch 16, 2018 - 4:02am

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 machine in combination with feature extraction and classification methods may be utilized.

This paper 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 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.

Publication Year: 
2018
Publication Volume: 
9
Publication Control Number: 
007
Page Count: 
14
Submission Keywords: 
Case-Based Reasoning
Fourth Order Statistics
Gaussian Mixture Model
Linear Regression and Moving Average Square Value filtering
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Industrial applications
Sensors
Submitted by: 
  
 
 
 

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