Real-Time Current-Based Distributed Bearing Faults Detection in Small Cooling Fan Motors

Developing a simple real-time bearing fault detection algorithm is essential for small cooling fan motors, ensuring power electronics systems and data center stability. Our study introduces a real-time, low-resource algorithm for microcontrollers, utilizing motor current data to detect the most common bearing faults in these applications. Notably, small motors exhibit significant current changes due to lubrication and contamination issues, unlike larger motors, where such changes are minimal. We comprehensively assess distributed bearing faults stemming from lubrication and contamination across seven motors under various conditions. These motors are tested under both no-load and fan-load scenarios at ten different speeds, with controlled aging of motor bearings. Key time-domain features, such as Root Mean Square (RMS), peak values, and crest factors of motor current, are scrutinized to create our proposed algorithm. We rigorously evaluate sensitivity, false detection scenarios, and compare our algorithm to a machine learning model. In practical experiments using the TI F280049 microcontroller, our algorithm outperforms, demanding minimal instruction cycles and memory resources. Achieving an accuracy rate exceeding 92% and consistently demonstrating an F1 score of over 90%, the algorithm is proven to be a robust and practical solution for precisely and rapidly detecting distributed bearing faults in small cooling fan motors.