Data-driven fault classification is complicated by imbalanced training data and unknown fault classes. Fault diagnosis of dynamic systems is done by detecting changes in time-series data, for example residuals, caused by faults or system degradation. Different fault classes can result in similar residual outputs, especially for small faults which can be difficult to distinguish from nominal system operation. Analyzing how easy it is to distinguish data from different fault classes is crucial during the design process of a diagnosis system to evaluate if classification performance requirements can be met. Here, a data-driven model of different fault classes is used based on the Kullback-Leibler divergence. This is used to develop a framework for quantitative fault diagnosis performance analysis and open set fault classification. A data-driven fault classification algorithm is proposed which can handle unknown faults and also estimate the fault size using training data from known fault scenarios. To illustrate the usefulness of the proposed methods, data have been collected from an engine test bench to illustrate the design process of a data-driven diagnosis system, including quantitative fault diagnosis analysis and evaluation of the developed open set fault classification algorithm.
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