Infrared thermography for condition monitoring – A review

Review of Unmanned Aerial System (UAS) applications in the built environment: Towards automated building inspection procedures using drones

Recent Progress in Diagnosing the Reliability of Electrical Equipment by Using Infrared Thermography

Infrared system engineering

A variety of reasons, specifically contact issues, irregular loads, cracks in insulation, defective relays, terminal junctions and other similar issues, increase the internal temperature of electrical instruments. This results in unexpected disturbances and potential damage to power equipment. Therefore, the initial prevention measures of thermal anomalies in electrical tools are essential to prevent power-equipment failure. In this article, we address this initial prevention mechanism for power substations using a computer-vision approach by taking advantage of infrared thermal images. The thermal images are taken through infrared cameras without disturbing the working operations of power substations. Thus, this article augments the non-destructive approach to defect analysis in electrical power equipment using computer vision and machine learning. We use a total of 150 thermal pictures of different electrical equipment in 10 different substations in operating conditions, using 300 different hotspots. Our approach uses multi-layered perceptron (MLP) to classify the thermal conditions of components of power substations into “defect” and “non-defect” classes. A total of eleven features, which are first-order and second-order statistical features, are calculated from the thermal sample images. The performance of MLP shows initial accuracy of 79.78%. We further augment the MLP with graph cut to increase accuracy to 84%. We argue that with the successful development and deployment of this new system, the Technology Department of Chongqing can arrange the recommended actions and thus save cost in repair and outages. This can play an important role in the quick and reliable inspection to potentially prevent power substation equipment from failure, which will save the whole system from breakdown. The increased 84% accuracy with the integration of the graph cut shows the efficacy of the proposed defect analysis approach.

https://www.mdpi.com/1996-1073/10/12/1987/pdf

Predictive Maintenance of Power Substation Equipment by Infrared Thermography Using a Machine-Learning Approach

Temperature and the resulting thermal behavior of electric power generation and distribution equipment and industrial electrical systems and processes are the most critical factors in the reliability of any operation or facility. Temperature is by far the most measured quantity in any industrial environment. For these reasons, monitoring the thermal operating condition of electrical and electromechanical equipment is considered to be key to increasing operational reliability and decreasing electrical losses.

/pdf/improving-reliability-and-decreasing-losses-of-electrical-16erdkg9p7.pdf

Michal Kolcun

Papers

Improving reliability and decreasing losses of electrical system with infrared thermography