Towards Differentiating Between Failures and Domain Shifts in Industrial Data Streams

TL;DR

This paper introduces a novel approach combining changepoint detection, domain adaptation, and explainable AI to distinguish between failures and normal domain shifts in industrial data streams, enhancing system robustness.

Contribution

It presents an integrated method that detects data distribution changes, differentiates failures from healthy shifts, and incorporates explainability for practical industrial applications.

Findings

Effective detection of domain shifts and failures in real-time

Successful application on steel factory data stream

Enhanced interpretability with XAI components

Abstract

Anomaly and failure detection methods are crucial in identifying deviations from normal system operational conditions, which allows for actions to be taken in advance, usually preventing more serious damages. Long-lasting deviations indicate failures, while sudden, isolated changes in the data indicate anomalies. However, in many practical applications, changes in the data do not always represent abnormal system states. Such changes may be recognized incorrectly as failures, while being a normal evolution of the system, e.g. referring to characteristics of starting the processing of a new product, i.e. realizing a domain shift. Therefore, distinguishing between failures and such ''healthy'' changes in data distribution is critical to ensure the practical robustness of the system. In this paper, we propose a method that not only detects changes in the data distribution and anomalies but also allows us to distinguish between failures and normal domain shifts inherent to a given process. The proposed method consists of a modified Page-Hinkley changepoint detector for identification of the domain shift and possible failures and supervised domain-adaptation-based algorithms for fast, online anomaly detection. These two are coupled with an explainable artificial intelligence (XAI) component that aims at helping the human operator to finally differentiate between domain shifts and failures. The method is illustrated by an experiment on a data stream from the steel factory.