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