Data Augmentation of Multivariate Sensor Time Series using Autoregressive Models and Application to Failure Prognostics
用自回归模型增强多变量传感器时间序列:故障预后应用
This work presents a novel data augmentation solution for non-stationary multivariate time series and its application to failure prognostics. The method extends previous work from the authors which is based on time-varying autoregressive processes. It can be employed to extract key information from a limited number of samples and generate new synthetic samples in a way that potentially improves the performance of PHM solutions. This is especially valuable in situations of…