论文标题
合奏分类器是否足够强大，可以检测和诊断中间 - 严重性断层？
Are Ensemble Classifiers Powerful Enough for the Detection and Diagnosis of Intermediate-Severity Faults?
论文作者
论文摘要
与严重的断层相比，中级性症状（IS）出现了温和的症状，由于它们与正常工作条件非常相似，因此更难检测和诊断。训练数据中缺乏的IS故障示例可能会对基于机器学习（ML）技术构建的故障检测和诊断（FDD）的严重风险构成严重的风险，因为这些故障很容易被误认为是正常的操作条件。集合模型被广泛应用于ML，被认为是检测分布（OOD）数据的有前途的方法。我们通过在两个现实世界数据集上的几个流行的集合模型进行了广泛的实验，在这些模型中识别出常见的陷阱。然后，我们讨论如何设计更有效的集合模型来检测和诊断是故障。
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.
