Rolling element bearings are commonly used in rotating machines to transmit rotation and power. On the other hand, bearing faults could be the most common reason for machinery imperfections. Although many techniques have been proposed in the literature to detect bearing faults, each has its own merits and limitations, and none of them can perform reliable bearing fault detection and diagnosis. The objective of this work is to develop adaptive neurofuzzy (ANF) technology to integrate merits from several bearing fault detection techniques for automatic bearing condition monitoring and fault diagnosis. Three fault detection techniques are selected to extract representative features: envelope spectrum analysis, wavelet energy transformation, and variable mode decomposition. The feature indices will be the inputs to the proposed ANF classifier. The ANF can first classify if the bearing is healthy or faulty. If a fault is present, it can predict the type of bearing fault present in the signal, such as defect on the outer race, inner race or rolling element. A new training method is proposed to improve classification convergence and training efficiency. Its effectiveness is examined experimentally.
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