Infrared Thermography for Fault Diagnosis in Electrical Machinery

Infrared thermography offers a non-invasive route to monitor electrical machinery by capturing surface temperature distributions that reflect internal operating conditions. Faults such as bearing friction, misalignment, loose connections and insulation breakdown generate distinctive thermal signatures that can be recorded remotely, in real time and without contact. Recent advances in sensor resolution, camera calibration and image-processing algorithms have enhanced sensitivity and reduced false alarms. When combined with pattern-recognition and machine-learning techniques, thermographic inspection supports predictive maintenance, minimises unplanned downtime and improves safety in applications ranging from industrial motors and gearboxes to portable power tools and substation equipment. Challenges remain in compensating for emissivity variations, ambient influences and standardisation of data-processing workflows, but the global drive towards Industry 4.0 and digital twins underpins continued growth in this field.

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Infrared Thermography for Fault Diagnosis in Electrical Machinery publication trend

The graph below shows the total number of articles in infrared thermography for fault diagnosis in electrical machinery across all publications each year (not limited to Nature Index journals).

Technical terms

Infrared thermography: Imaging technique that captures surface temperature distributions based on emitted infrared radiation.

Thermal image: Spatial map of temperature variations across a surface visualised through false-colour representation.

Feature extraction: Process of quantifying salient thermal patterns from images for input to classification algorithms.

Convolutional neural network (CNN): Deep-learning model employing convolution and pooling layers to learn hierarchical image features.

Support vector machine (SVM): Supervised learning algorithm that separates data into classes by optimising a hyperplane in feature space.

Domain adaptation: Technique to transfer learned features from one data domain (such as infrared imagery) to another (such as vibration signals) for robust classification.

References

1. Ventilation Diagnosis of Angle Grinder Using Thermal Imaging. Sensors (2021).
2. Thermographic Fault Diagnosis of Shaft of BLDC Motor. Sensors (2022).
3. Fusion Domain-Adaptation CNN Driven by Images and Vibration Signals for Fault Diagnosis of Gearbox Cross-Working Conditions. Entropy (2022).