A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection
This work addresses the problem of identifying incipient faults in systems for engineers and practitioners, offering a practical solution for scenarios with limited training data, though it is incremental as it builds on existing OC-SVM methods.
The paper tackles the challenge of calibrating One-Class Support Vector Machines (OC-SVM) for change point detection in time series, particularly for system health monitoring, and demonstrates that the calibrated OC-SVM achieves satisfactory accuracy with fewer training data compared to state-of-the-art deep learning methods on the C-MAPSS dataset.
It is important to identify the change point of a system's health status, which usually signifies an incipient fault under development. The One-Class Support Vector Machine (OC-SVM) is a popular machine learning model for anomaly detection and hence could be used for identifying change points; however, it is sometimes difficult to obtain a good OC-SVM model that can be used on sensor measurement time series to identify the change points in system health status. In this paper, we propose a novel approach for calibrating OC-SVM models. The approach uses a heuristic search method to find a good set of input data and hyperparameters that yield a well-performing model. Our results on the C-MAPSS dataset demonstrate that OC-SVM can also achieve satisfactory accuracy in detecting change point in time series with fewer training data, compared to state-of-the-art deep learning approaches. In our case study, the OC-SVM calibrated by the proposed model is shown to be useful especially in scenarios with limited amount of training data.