  Incipient anomalies present milder symptoms compared to severe ones, and are
more difficult to detect and diagnose due to their close resemblance to normal
operating conditions. The lack of incipient anomaly examples in the training
data can pose severe risks to anomaly detection methods that are built upon
Machine Learning (ML) techniques, because these anomalies can be easily
mistaken as normal operating conditions. To address this challenge, we propose
to utilize the uncertainty information available from ensemble learning to
identify potential misclassified incipient anomalies. We show in this paper
that ensemble learning methods can give improved performance on incipient
anomalies and identify common pitfalls in these models through extensive
experiments on two real-world datasets. Then, we discuss how to design more
effective ensemble models for detecting incipient anomalies.
