Fault Diagnosis of Heavy Duty Machines : Automatic Transmission Clutches

This paper presents a generic approach to fault diagnosis of heavy duty machines that combines signal processing, statistics, machine learning, and case-based reasoning for on-board and off-board analysis. The used methods complement each other in that the on-board methods are fast and light-weight, while case-based reasoning is used off-board for fault diagnosis and for retrieving cases as support in manual decision making. Three major contributions are novel approaches to detecting clutch slippage, anomaly detection, and case-based diagnosis that is closely integrated with the anomaly detection model. As example application, the proposed approach has been applied to diagnosing the root cause of clutch slippage in automatic transmissions.

[1]  T. L. Paez,et al.  Statistical measures of clipped random signals , 1986 .

[2]  Luc Lamontagne,et al.  Case-Based Reasoning Research and Development , 1997, Lecture Notes in Computer Science.

[3]  L. T. DeCarlo.  On the meaning and use of kurtosis. , 1997 .

[4]  Lars Håkansson,et al.  Identification of dynamic properties of boring bar vibrations in a continuous boring operation , 2004 .

[5]  Pulak Bandyopadhyay,et al.  An integrated framework for effective service and repair in the automotive domain: An application of association mining and case-based-reasoning , 2011, Comput. Ind..

[6]  John Lindström,et al.  Functional Products: Business Model Elements , 2013 .

[7]  Klaus-Dieter Althoff,et al.  Knowledge acquisition in the domain of CNC machining centers: the Moltke approach , 1999 .

[8]  Cesare Furlanello,et al.  minerva and minepy: a C engine for the MINE suite and its R, Python and MATLAB wrappers , 2012, Bioinform..

[9]  Roland Larsson,et al.  Water contamination effect in wet clutch system , 2013 .

[10]  David McSherry,et al.  Introduction to the Special Issue on Explanation in Case-Based Reasoning , 2005, Artificial Intelligence Review.

[11]  Niklas Lingesten.  Wear behavior of wet clutches , 2012 .

[12]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[13]  Richard M. Crowder,et al.  Integrating case-based reasoning and hypermedia documentation: an application for the diagnosis of a welding robot at Odense steel shipyard , 1999 .

[14]  Jerry M. Mendel,et al.  Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications , 1991, Proc. IEEE.

[15]  Tomas Olsson,et al.  Case-Based Reasoning for Explaining Probabilistic Machine Learning , 2014 .

[16]  Rikard Mäki.  Wet Clutch Tribology - Friction Characteristics in Limited Slip Differentials , 2005 .

[17]  Randika Koswatta,et al.  Moving average filtering technique for signal processing in digital section of UWB chipless RFID reader , 2010, 2010 Asia-Pacific Microwave Conference.

[18]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[19]  Peter Funk,et al.  Fault diagnosis in industry using sensor readings and case-based reasoning , 2004, J. Intell. Fuzzy Syst..

[20]  J. Bendat,et al.  Random Data: Analysis and Measurement Procedures , 1987 .

[21]  R. C. Thomas,et al.  The cognitive role of an engineer in a diagnostic task , 1990 .

[22]  Stefan Wess,et al.  MOLTKE-AN INTEGRATED WORKBENCH FOR FAULT DIAGNOSIS IN ENGINEERING SYSTEMS , 2003 .

[23]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[24]  Peter Funk,et al.  Fault Diagnosis of Industrial Robots Using Acoustic Signals and Case-Based Reasoning , 2004, ECCBR.

[25]  Bo-Suk Yang,et al.  Integration of ART-Kohonen neural network and case-based reasoning for intelligent fault diagnosis , 2004, Expert Syst. Appl..

[26]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[27]  Tomas Olsson,et al.  Explaining Probabilistic Fault Diagnosis and Classification Using Case-Based Reasoning , 2014, ICCBR.

[28]  Feng Xue,et al.  Operational Data Based Anomaly Detection for Locomotive Diagnostics , 2006, MLMTA.