Data Augmentation of Sensor Time Series using Time-varying Autoregressive Processes

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Published Oct 26, 2023
Douglas Baptista de Souza Bruno Paes Leao

Abstract

This work presents a novel data-centric solution for fault diagnostics and failure prognostics consisting of a data-augmentation method which is well suited for non-stationary mutivariate time-series data. The method, based on time-varying autoregressive processes, can be employed to extract key information from a limited number of samples and generate new artificial samples in a way that benefits the development of diagnostics and prognostics solutions. The proposed approach is tested based on three real-world datasets associated with failure diagnostics problems using two types of machine learning methods. Results indicate the proposed method improves performance in all tested cases.

How to Cite

Baptista de Souza, D., & Paes Leao, B. (2023). Data Augmentation of Sensor Time Series using Time-varying Autoregressive Processes. Annual Conference of the PHM Society, 15(1). https://doi.org/10.36001/phmconf.2023.v15i1.3565
Abstract 200 | PDF Downloads 212

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Keywords

data augmentation, data-centric, non-stationary, time-varying, autoregressive

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Section
Technical Research Papers