Unsupervised Representation Learning in Multivariate Time Series with Simulated Data

Multivariate time-series data contains valuable information, but are challenging to analyze and model. This study uses unsupervised representation learning approaches to extract meaningful representations from unlabeled data, which are used to identify patterns of underlying states. These representations can be later utilized as inputs in downstream tasks for prognosis and health management (PHM) of complex physical systems, with the aim of quantifying system’s reliability and efficiency, and measuring the potential for failure, reducing downtime, and improving overall safety. We evaluate the performance of three advanced methods, namely Temporal Neighborhood Coding (TNC), Triplet Loss, and Contrastive Predictive Coding (CPC), on three simulated scenarios that mimic real-world situations. Key Performance Indicators are used as evaluation metrics for clustering and classification tasks. Our objective is to demonstrate the practicality of these approaches in multiple scenarios while remaining domain agnostic.