Constraint Guided AutoEncoders for Joint Optimization of Condition Indicator Estimation and Anomaly Detection in Machine Condition Monitoring

TL;DR

This paper introduces an extension to Constraint Guided AutoEncoders that jointly performs anomaly detection and condition indicator estimation, ensuring monotonic CI predictions for better machine condition monitoring.

Contribution

The proposed extension enables a single model to perform both anomaly detection and monotonic condition indicator estimation, improving CI behavior while maintaining detection performance.

Findings

The method performs comparably or slightly better than CGAE in anomaly detection.

It enforces monotonic increase in CI predictions over time.

Experimental results validate improved CI monotonicity without sacrificing detection accuracy.

Abstract

The main goal of machine condition monitoring is, as the name implies, to monitor the condition of industrial applications. The objective of this monitoring can be mainly split into two problems. A diagnostic problem, where normal data should be distinguished from anomalous data, otherwise called Anomaly Detection (AD), or a prognostic problem, where the aim is to predict the evolution of a Condition Indicator (CI) that reflects the condition of an asset throughout its life time. When considering machine condition monitoring, it is expected that this CI shows a monotonic behavior, as the condition of a machine gradually degrades over time. This work proposes an extension to Constraint Guided AutoEncoders (CGAE), which is a robust AD method, that enables building a single model that can be used for both AD and CI estimation. For the purpose of improved CI estimation the extension incorporates a constraint that enforces the model to have monotonically increasing CI predictions over time. Experimental results indicate that the proposed algorithm performs similar, or slightly better, than CGAE, with regards to AD, while improving the monotonic behavior of the CI.