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A combined diagnosis system design using model-based and data-driven techniques

A hybrid diagnosis system design is proposed that combines model-based and data-driven diagnosis methods for fault isolation. A set of residuals are used to detect if there is a fault in the system and a consistency-based fault isolation algorithm is used to compute all diagnosis candidates that can explain the triggered residuals. To improve fault isolation, diagnosis candidates are ranked by evaluating the residuals using a set of one-class support vector machines trained using data from different faults. The proposed diagnosis system design is evaluated using simulations of a model describing the air-flow in an internal combustion engine.

Daniel Jung, Kok Ng, Erik Frisk and Mattias Krysander

Conference on Control and Fault-Tolerant Systems (SysTol'16), 2016

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