Are Ensemble Classifiers Powerful Enough for the Detection and Diagnosis of Intermediate-Severity Faults?
This addresses a critical safety issue in fault detection for industrial systems, but it is incremental as it builds on existing ensemble methods.
The paper tackled the problem of detecting and diagnosing intermediate-severity faults, which are difficult due to their resemblance to normal conditions and lack of training data, by identifying pitfalls in ensemble models through experiments on two real-world datasets and proposing design improvements.
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.