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Updated: Jan 7, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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MIRA: A Transformer-Based Framework for Idler Roller Anomaly Detection and Localization.

Younho Nam¹, Su Yeon Shim², Kyeong Su Shin²

¹Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea.

Sensors (Basel, Switzerland)

|December 31, 2025

Summary

This study introduces MIRA, a transformer-based system for detecting and locating idler roller faults in conveyor systems using sound and vibration data. MIRA achieves high accuracy in anomaly detection and fault localization, improving industrial safety and efficiency.

Keywords:

anomaly detection belt conveyor idler condition monitoring transformer autoencoder unsupervised learning

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Area of Science:

Industrial Engineering
Machine Learning
Sensor Technology

Background:

Effective monitoring of belt conveyor idlers is crucial for operational safety and efficiency.
Current monitoring methods face challenges with scalability and timely fault detection, especially in varying conditions.

Purpose of the Study:

To develop a robust and scalable framework for detecting and localizing idler roller anomalies in industrial conveyor systems.
To utilize low-cost acoustic and vibration sensors for comprehensive condition monitoring.

Main Methods:

Proposed MIRA, a transformer-based framework utilizing a masked autoencoder for unsupervised anomaly detection.
Employed acoustic and vibration signals from sensors to reconstruct normal operational patterns and identify deviations.
Developed fault localization capabilities without requiring labeled data.

Main Results:

MIRA demonstrated high performance on a custom conveyor belt testbed.
Achieved 98.70% accuracy in anomaly detection and 96.09% in fault localization across various fault types, locations, and speeds.
Validated robustness and practical applicability in complex industrial settings.

Conclusions:

MIRA offers a scalable and accurate solution for conveyor belt idler monitoring.
The unsupervised, transformer-based approach enhances fault detection and localization capabilities.
The system shows significant potential for improving industrial conveying system maintenance and safety.