Condition Monitoring Method for Automatic Transmission Clutches

Agusmian Partogi Ompusunggu, Jean-Michel Papy, Steve Vandenplas, Paul Sas, and Hendrik Van Brussel
Publication Target: 
IJPHM
Publication Issue: 
1
Submission Type: 
Full Paper
Supporting Agencies (optional): 
Flanders' MECHATRONICS Technology Centre (FMTC)
AttachmentSizeTimestamp
ijPHM_12_003.pdf4.47 MBJune 1, 2012 - 2:51pm

This paper presents the development of a condition monitoring method for wet friction clutches which might be useful for automatic transmission applications. The method is developed based on quantifying the change of the relative rotational velocity signal measured between the input and output shaft of a clutch. Prior to quantifying the change, the raw velocity signal is preprocessed to capture the relative velocity signal of interest. Three dimensionless parameters, namely the normalized engagement duration, the normalized Euclidean distance and the spectral angle mapper distance, that can be easily extracted from the signal of interest are proposed in this paper to quantify the change. In order to experimentally evaluate and verify the potential of the proposed method, clutches' life data obtained by conducting accelerated life tests on some commercial clutches with different lining friction materials using a fully instrumented SAE#2 test setup, are utilized for this purpose. The aforementioned parameters extracted from the experimental data exhibit clearly progressive changes during the clutch service life and are well correlated with the evolution of the mean coefficient of friction (COF), which can be seen as a reference feature. Hence, the quantities proposed in this paper can therefore be seen as principle features that may enable us to monitor and assess the condition of wet friction clutches.

Publication Year: 
2012
Publication Volume: 
3
Publication Control Number: 
003
Page Count: 
14
Submission Keywords: 
Wet friction clutches
on-line condition monitoring
automatic transmissions
dissimilarity measures
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Industrial applications
  
 
 
 

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