Dynamic classifier auditing by unsupervised anomaly detection methods: an application in packaging industry predictive maintenance

TL;DR

This paper introduces an expert system that combines a classifier with unsupervised anomaly detection methods to improve predictive maintenance decision-making for packaging machines in manufacturing.

Contribution

It proposes a novel hybrid approach integrating classifier and anomaly detection ensemble to enhance maintenance decision accuracy.

Findings

Anomaly detection methods improve classifier performance.

Majority voting ensemble yields the best F1 score.

The system enables automatic, sensor-based maintenance decisions.

Abstract

Predictive maintenance in manufacturing industry applications is a challenging research field. Packaging machines are widely used in a large number of logistic companies' warehouses and must be working uninterruptedly. Traditionally, preventive maintenance strategies have been carried out to improve the performance of these machines. However, this kind of policies does not take into account the information provided by the sensors implemented in the machines. This paper presents an expert system for the automatic estimation of work orders to implement predictive maintenance policies for packaging machines. The key idea is that, from a set of alarms related to sensors implemented in the machine, the expert system should take a maintenance action while optimizing the response time. The work order estimator will act as a classifier, yielding a binary decision of whether a machine must undergo a maintenance action by a technician or not, followed by an unsupervised anomaly detection-based filtering stage to audit the classifier's output. The methods used for anomaly detection were: One-Class Support Vector Machine (OCSVM), Minimum Covariance Determinant (MCD) and a majority (hard) voting ensemble of them. All anomaly detection methods improve the performance of the baseline classifer but the best performance in terms of F1 score was obtained by the majority voting ensemble.