Diagnosis of stator unbalance in asynchronous machines using themography and statistics

Innovation in maintenance practices and responsible resource management can play a key role in achieving global sustainable development goals. The integration of statistical analysis of thermographic data for fault diagnosis of three-phase induction motors (IMs) not only improves their performance, but also makes a significant contribution to the achievement of various Sustainable Development Goals (SDGs). Infrared thermography (IRT) is a non-contact, cost-effective and efficient method for real-time temperature monitoring and fault detection in electrical and mechanical machines. This paper examines the use of IRT for the diagnosis of stator unbalance (SU) in a IM. In this experimental work, thermal images were captured using a thermal imaging camera for a healthy-state IM, and the thermograms were then converted into usable data for in-depth statistical analysis. Measurements were carried out in three different conditions: healthy state, stator unbalance, and missing a phase. The results obtained were then compared using various statistical parameters, notably the skewness coefficient (S<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</inf>), to evaluate the effectiveness of this thermographic and statistical method for diagnosing unbalance. The analyses show that the statistical description of the temperature distribution in the stator region of interest (ROI) is highly significant. Indeed, the values of the various parameters used clearly distinguish an unbalanced state from a healthy one. More specifically, an S<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</inf> whose absolute value exceeds 2 is a reliable indicator of SU.