Abstract
Bearing fault diagnosis is of utmost importance in the maintenance of mechanical equipment. The collected fault vibration signal generally presents a modulated nature due to the special structure and dynamic characteristics of the bearings. This paper introduces a novel demodulation analysis technique via energy separation and local low-rank matrix approximation (LLORMA) to address this type of signal. The amplitude envelope and instantaneous frequency of the signal can be calculated via an energy separation algorithm based on the Teager energy operator. We can confirm the bearing faults by comparing the peak frequencies of the Fourier spectrum of the amplitude envelope and instantaneous frequency with the theoretical bearing fault-related frequencies. However, this algorithm is only suitable for handling single-component signals. In addition, the powerful background noise has a serious effect on the demodulation results. To tackle these problems, a new signal decomposition method based on LLORMA is proposed to decompose the signal into several single-components and eliminate the noise simultaneously. After that, the single-component signal representing the fault characteristics can be identified via the high frequency feature of the modulated signal. The analysis of the simulated signal and the bearing outer race fault signal collected from a bearing-gear fault test rig indicate that the proposed technique has an excellent diagnostic performance for bearing fault signals.