A Smart Sensor-Based Bearing Fault Detection Technology

Rolling element bearings are commonly used in rotating machines. However, reliable and real-time bearing condition monitoring and fault diagnosis is a challenging problem in industry, due to limitations associated with reliable real-time data acquisition (DAQ) and signal processing in noisy environments where bearings are often housed. In this article, a smart sensor-based bearing fault detection technology is proposed. The proposed method introduces a DAQ design for high resolution vibration signal measurement. A wavelet swarm decomposition (WSD) technique is also developed for stand-alone bearing fault detection. The proposed WSD technique adopts a new wavelet filter to adaptively determine center frequencies in the time-frequency domain. Moreover, an ensemble function is introduced to select decomposition modes and improve processing efficiency. The effectiveness of the proposed smart sensor bearing fault detection method is validated experimentally to detect different types of bearing faults. Test results show that the proposed method powered by the developed WSD technique performs better (6% higher) than other related techniques in bearing fault detection and diagnosis.