Cross-Machine Anomaly Detection Leveraging Pre-trained Time-series Model

TL;DR

This paper introduces a cross-machine anomaly detection framework using a pre-trained time-series model and domain-invariant features, improving generalization across different machines in manufacturing settings.

Contribution

It proposes a novel method combining MOMENT embeddings with Random Forest classifiers to extract machine-invariant features for anomaly detection.

Findings

Outperforms baseline methods in cross-machine anomaly detection accuracy.

Effectively disentangles machine-related and condition-related features.

Demonstrates robustness across multiple industrial machines.

Abstract

Achieving resilient and high-quality manufacturing requires reliable data-driven anomaly detection methods that are capable of addressing differences in behaviors among different individual machines which are nominally the same and are executing the same processes. To address the problem of detecting anomalies in a machine using sensory data gathered from different individual machines executing the same procedure, this paper proposes a cross-machine time-series anomaly detection framework that integrates a domain-invariant feature extractor with an unsupervised anomaly detection module. Leveraging the pre-trained foundation model MOMENT, the extractor employs Random Forest Classifiers to disentangle embeddings into machine-related and condition-related features, with the latter serving as representations which are invariant to differences between individual machines. These refined features enable the downstream anomaly detectors to generalize effectively to unseen target machines. Experiments on an industrial dataset collected from three different machines performing nominally the same operation demonstrate that the proposed approach outperforms both the raw-signal-based and MOMENT-embedding feature baselines, confirming its effectiveness in enhancing cross-machine generalization.