Data Augmentation of Multivariate Sensor Time Series using Autoregressive Models and Application to Failure Prognostics

TL;DR

This paper introduces a novel data augmentation method using autoregressive models for multivariate sensor time series, significantly enhancing failure prognostics performance in data-scarce scenarios.

Contribution

It extends previous autoregressive-based augmentation techniques to non-stationary multivariate time series for improved prognostics in PHM applications.

Findings

Improves PHM solution accuracy on CMAPSS dataset

Enhances data efficiency in failure prognostics

Demonstrates significant performance gains with augmentation

Abstract

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 data scarcity which are very usual in PHM, especially for failure prognostics. The proposed approach is tested based on the CMAPSS dataset, commonly employed for prognostics experiments and benchmarks. An AutoML approach from PHM literature is employed for automating the design of the prognostics solution. The empirical evaluation provides evidence that the proposed method can substantially improve the performance of PHM solutions.