Transfer Learning for Anomaly Detection in Rotating Machinery Using Data-Driven Key Order Estimation

The detection of anomalous behavior of an engineered system or its components is an important task for enhancing reliability, safety, and efficiency across various engineering applications. However, designing an accurate anomaly detector can be very challenging in settings where anomalous labels are sparse or, in the worst case, missing in the training data. To mitigate this issue of the lack of anomalous labels in the domain of interest, existing approaches use transfer learning by leveraging information from anomalous samples in a closely related source domain. Although previous studies have shown good results from applying transfer learning, they do not specifically address the issue of high false-positive rates from such transfer. High false-positive rates can arise from misleading information present in uninformative/irrelevant features. Inspired by this observation, this paper focuses on identifying key input features, termed as such, due to their strong predictability in anomaly detection. A transfer learning approach is introduced that leverages the optimal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f_{\beta } $ </tex-math></inline-formula> score for key feature estimation. This approach involves finding a weight vector to amplify key features and attenuate uninformative inputs during prediction. We demonstrate the capabilities of our proposed anomaly detection method as a quality check for newly manufactured automotive transmissions. Given the use of frequency domain order-based features in our use case, our proposed method is also easily extensible to the anomaly detection of other rotating machinery. Based on our findings we also find that our proposed anomaly detection algorithm, utilizing precise data-driven features, outperforms detectors based on experience/heuristics-based features currently used in automotive engineering applications. More importantly, our proposed framework can work with any downstream unsupervised anomaly detection algorithm, allowing us to freely choose the best algorithm for the anomaly detection task on hand. Note to Practitioners—Unlike legacy systems that have rich data sets for both healthy and anomalous behaviors over a broad spectrum of operating states, newly designed system or design variants of existing systems have little or no performance data. This makes the task of identifying indicators of anomalous behaviors very challenging for systems without legacy information. This work introduces a machine learning framework that uses label information from related, well-studied systems, to improve anomaly detection outcomes in a new or variant (but related) system. We demonstrate the effectiveness of our transfer approach through the End of Line (EoL) anomaly detection for quality checks on new design variants of an existing automotive transmission. Inspired by feature selection techniques, our proposed framework is easy to use and offers excellent interpretability, which is crucial for achieving a zero-failure system design.