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It is important to identify the change point of a system's health status,which usually signifies an incipient fault under development. The One-ClassSupport Vector Machine (OC-SVM) is a popular machine learning model for anomalydetection and hence could be used for identifying change points; however, it issometimes difficult to obtain a good OC-SVM model that can be used on sensormeasurement time series to identify the change points in system health status.In this paper, we propose a novel approach for calibrating OC-SVM models. Theapproach uses a heuristic search method to find a good set of input data andhyperparameters that yield a well-performing model. Our results on the C-MAPSSdataset demonstrate that OC-SVM can also achieve satisfactory accuracy indetecting change point in time series with fewer training data, compared tostate-of-the-art deep learning approaches. In our case study, the OC-SVMcalibrated by the proposed model is shown to be useful especially in scenarioswith limited amount of training data.

A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection