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One of the common ways to perform data-driven fault diagnosis is to employ statistical models, which can classify the data into nominal (healthy) and a fault class or distinguish among different fault classes. The former is termed fault (anomaly) detection, and the latter is termed fault isolation (classification, diagnosis). Traditionally, statistical classifiers are trained using data from faulty and nominal behaviors in a batch mode. However, it is difficult to anticipate, a priori, all the possible ways in which failures can occur, especially when a new vehicle model is introduced. Therefore, it is imperative that diagnostic algorithms adapt to new cases on an ongoing basis. In this paper, a unified methodology to incrementally learn new information from evolving databases is presented. The performance of adaptive (or incremental learning) classification techniques is discussed when: 1) the new data has the same fault classes and same features and 2) the new data has new fault classes, but with the same set of observed features. The proposed methodology is demonstrated on data sets derived from an automotive electronic throttle control subsystem.

Incremental Classifiers for Data-Driven Fault Diagnosis Applied to Automotive Systems