A novel fault detection method for rotating machinery based on self-supervised contrastive representations

Abstract

In many industrial applications, only healthy condition monitoring data is available for rotating machines that are newly put into operation or have not met the onset of degradation, i.e. the occurrence of incipient failures, after a long-time run. Fault detection based on only the healthy condition data can be a challenging one-class classification problem, whose key is to extract high-quality representations of healthy condition which can be distinguished from those of faulty conditions to the maximum extent. However, the widely adopted hand-crafted representations composed of statistical metric values and representations extracted by data reconstruction methods cannot guarantee to be faithful to the fault detection task, because their targets are indirect regarding the extraction of discriminative representations. In this work, we propose a novel fault detection framework based on i) the self-supervised representations extracted from the vibration signals, the most widely used for rotating machine monitoring, of healthy condition using contrastive learning and ii) one-class classifiers. A negative sequence construction strategy is developed, which allows the extraction of intrinsic representations of healthy condition data using contrastive learning by distinguishing a data sample from its augmentations. The extracted representation is sensitive to the modifications of healthy data and, therefore, is more faithful to the one-class classification task. The performance of the proposed framework is verified using experimental data of bearings and gears, which are key vulnerable components of rotating machines. Its advantage is shown by comparing it with state-of-the-art methods based on other unsupervised representations and one-class classifiers.