Unbalance Fault Diagnosis in Electric Motors under Non-Stationary Load Conditions: An Iterative Filter Decomposition Approach

In electric vehicles, the dynamic nature of driving conditions, speed variations, and fluctuations in loads introduce non-stationary loads on electric motors. These non-uniform load distributions can result in mechanical imbalance faults within the motor, leading to increased vibrations, reduced motor performance, and potential mechanical stress on its components. The development of advanced diagnostic methods is crucial for detecting and addressing these mechanical balance faults in electric motors. This article introduces a novel approach for diagnosing mechanical unbalance faults using local iterative filter decomposition, specifically designed for electric motors operating under non-stationary load conditions. The experimental results demonstrate that the application of iterative filter decomposition yields superior outcomes compared to the absence of decomposition. By employing this method, the algorithm achieves a classification accuracy exceeding 80% for all types of unbalances, including the detection of low-speed rotation unbalance faults, which are known to be challenging to identify.