Early classification for bearing faults of rotating machinery based on MFES and Bayesian network

Bearing faults of rotating machinery are observed as impulses in the vibration signal, but it is mostly immersed in noise. In order to effectively remove this noise and detect the impulses, a novel technique with multiple frequency energy spectrum (MFES) and Bayesian network(BN) inference is proposed in this paper. Original acceleration signals are processed by fast Fourier transformation (FFT) from the time domain to frequency domain. According to the analysis of the frequency information, the MFES is put forward to extract features from vibration under normal and faulty conditions of rotational mechanical systems. These features were given as inputs for training and testing the BN model. By existing BN inference algorithms, and the inference result for fault diagnosis is provided. With BN inference algorithms being coupled to this new technique, it makes the presented approach be able to detect early faults. Experimental results show that the proposed approach is effective and robust in bringing out the early bearing fault classification of rotating machinery.