A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection
A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection
复制标题
DOI：
10.1109/icphm.2019.8819385
复制
发表时间：
2019-02
期刊：
影响因子：
--
通讯作者：
Baihong Jin;Yuxin Chen;Dan Li;K. Poolla;A. Sangiovanni-Vincentelli
中科院分区：
文献类型：
--
作者：
Baihong Jin;Yuxin Chen;Dan Li;K. Poolla;A. Sangiovanni-Vincentelli
识别系统健康状态的变化点非常重要。实际上，变更点通常表示开发中的早期故障。一类支持向量机（OC-SVM）是一种流行的异常检测机器学习模型，可用于识别变化点；然而，有时很难获得一个好的OC-SVM模型，该模型可以用于传感器测量时间序列来识别系统健康状态的变化点。本文提出了一种校正OC-SVM模型的新方法。我们的方法使用启发式搜索方法来找到一组良好的输入数据和超参数，从而产生一个性能良好的模型。我们在C-MAPSS数据集上的结果表明，与最先进的深度学习方法相比，OC-SVM在使用更少的训练数据检测时间序列中的变化点方面可以达到令人满意的精度。在我们的案例研究中，由所提出的模型校准的OC-SVM被证明是有用的，特别是在训练数据量有限的情况下。
Identifying the change point of a system’s health status is important. Indeed, a change point usually signifies an incipient fault under development. The One-Class Support Vector Machine (OC-SVM) is a popular machine learning model for anomaly detection that 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. Our 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 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.