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Intermediate-Severity (IS) faults present milder symptoms compared to severefaults, and are more difficult to detect and diagnose due to their closeresemblance to normal operating conditions. The lack of IS fault examples inthe training data can pose severe risks to Fault Detection and Diagnosis (FDD)methods that are built upon Machine Learning (ML) techniques, because thesefaults can be easily mistaken as normal operating conditions. Ensemble modelsare widely applied in ML and are considered promising methods for detectingout-of-distribution (OOD) data. We identify common pitfalls in these modelsthrough extensive experiments with several popular ensemble models on tworeal-world datasets. Then, we discuss how to design more effective ensemblemodels for detecting and diagnosing IS faults.

Are Ensemble Classifiers Powerful Enough for the Detection and Diagnosis of Intermediate-Severity Faults?