Deep Learning for Infrared Thermal Image Based Machine Health Monitoring

Abstract

The condition of a machine can automatically be identified by creating and classifying features that summarize characteristics of measured signals. Currently, experts, in their respective fields, devise these features based on their knowledge. Hence, the performance and usefulness depends on the expert's knowledge of the underlying physics, or statistics. Furthermore, if new and additional conditions should be detectable, experts have to implement new feature extraction methods. To mitigate the drawbacks of feature engineering, a method from the sub-field of feature learning, i.e. deep learning, more specifically convolutional neural networks, is researched in this article. The objective of this article is to investigate if and how deep learning can be applied to infrared thermal video to automatically determine the condition of the machine. By applying this method on infrared thermal data in two use cases, i.e. machine fault detection and oil level prediction, we show that the proposed system is able to detect many conditions in rotating machinery very accurately (i.e. 95 % and 91.67 % accuracy for the respective use cases) without requiring any detailed knowledge about the underlying physics, and thus having the potential to significantly simplify condition monitoring using complex sensor data. Furthermore, we show that by using the trained neural networks, important regions in the infrared thermal images can be identified related to specific conditions which can potentially lead to new physical insights.

Vibration‐Based Fault Diagnosis of Commutator Motor

“…Condition monitoring of electric motors was developed for measurement and analysis of diagnostic signals such as acoustic [1,2], thermal [3,4], electric current [5][6][7], and vibration [8][9][10][11][12][13]. Each type of signal has advantages and disadvantages.…”

“…However, industrial use of technique-based thermal imaging gained a noticeable attention [3]. Inter-turn faults and cooling system faults were analysed in [3]. e analysis was conducted for induction motors.…”

“…e authors developed two thermal profiles indicators. Developed profiles indicators were used for analysis of thermal distributions [3].…”

“…Vibration signals are also acquired with low noise level (from environment or other machines). Vibration analysis is used for detection of mechanical and electrical faults such as rotor, stator faults, bearings, misalignment, and faults of gear transmission systems [1,3,8]. A review about fault diagnosis methods for gear transmission systems using vibration analysis was presented [8].…”

Deep learning for pyrolysis reactor monitoring: From thermal imaging toward smart monitoring system

“…The deep convolutional neural networks (DCNNs) have been applied for crack detection in both pavement 15 and nuclear power plants 16 from cameras. Besides, the DCNN has also been applied in machine health monitoring 17 based on infrared photos.…”