A textual transform of multivariate time-series for prognostics

TL;DR

This paper introduces a novel textual representation of multivariate time-series data for prognostics, enabling domain-agnostic, accurate, and interpretable early fault detection in industrial equipment.

Contribution

It presents a new textual transform method for multivariate time-series data, leveraging text-mining techniques for improved prognostics performance.

Findings

Superior prediction accuracy on benchmark datasets

Longer lead times to fault detection

Enhanced interpretability of prognostic models

Abstract

Prognostics or early detection of incipient faults is an important industrial challenge for condition-based and preventive maintenance. Physics-based approaches to modeling fault progression are infeasible due to multiple interacting components, uncontrolled environmental factors and observability constraints. Moreover, such approaches to prognostics do not generalize to new domains. Consequently, domain-agnostic data-driven machine learning approaches to prognostics are desirable. Damage progression is a path-dependent process and explicitly modeling the temporal patterns is critical for accurate estimation of both the current damage state and its progression leading to total failure. In this paper, we present a novel data-driven approach to prognostics that employs a novel textual representation of multivariate temporal sensor observations for predicting the future health state of the monitored equipment early in its life. This representation enables us to utilize well-understood concepts from text-mining for modeling, prediction and understanding distress patterns in a domain agnostic way. The approach has been deployed and successfully tested on large scale multivariate time-series data from commercial aircraft engines. We report experiments on well-known publicly available benchmark datasets and simulation datasets. The proposed approach is shown to be superior in terms of prediction accuracy, lead time to prediction and interpretability.