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.