DAT: A robust Discriminant Analysis-based Test of unimodality for unknown input distributions

Abstract

This letter introduces a Discriminant Analysis-based unimodality Test (DAT) for automatically identifying whether a dataset is unimodal or multimodal, detecting deviations in time series datasets, estimating statistical parameters, and identifying the skewness of the data. DAT is effective in classifying datasets under both unimodal and multimodal conditions and is suitable for bi-classification applications. The performance of DAT was compared to two well-known unimodality tests, namely the dip and folding tests, and is shown to perform better. We then extended DAT to the development of a fault detection technique, which was tested against five different machine learning classifiers using data from the Case Western Reserve University (CWRU) ball bearing database. The results obtained show that the proposed approach is effective, achieving 99.999% accuracy for detecting small ball bearing fault sizes (0.007 inches). Our conclusion indicates a significant potential of the proposed test for improving anomaly detection in industrial and other related fields.