Real-Time Predictive Maintenance using Autoencoder Reconstruction and Anomaly Detection

TL;DR

This paper presents a real-time machine learning-based anomaly detection system for predictive maintenance that models normal machine behavior and provides early fault warnings, reducing false positives and enabling timely interventions.

Contribution

The paper introduces an autoencoder-based anomaly detection method that automatically sets thresholds for fault severity, improving early fault detection in industrial machinery.

Findings

Effective detection of anomalies within the amber warning range

Early alarms can be raised before machine failure occurs

Reduces false positives compared to traditional threshold methods

Abstract

Rotary machine breakdown detection systems are outdated and dependent upon routine testing to discover faults. This is costly and often reactive in nature. Real-time monitoring offers a solution for detecting faults without the need for manual observation. However, manual interpretation for threshold anomaly detection is often subjective and varies between industrial experts. This approach is ridged and prone to a large number of false positives. To address this issue, we propose a Machine Learning (ML) approach to model normal working operation and detect anomalies. The approach extracts key features from signals representing known normal operation to model machine behaviour and automatically identify anomalies. The ML learns generalisations and generates thresholds based on fault severity. This provides engineers with a traffic light system were green is normal behaviour, amber is worrying and red signifies a machine fault. This scale allows engineers to undertake early intervention measures at the appropriate time. The approach is evaluated on windowed real machine sensor data to observe normal and abnormal behaviour. The results demonstrate that it is possible to detect anomalies within the amber range and raise alarms before machine failure.