An adaptive morphological filtering and feature enhancement method for spindle motor bearing fault diagnosis

Abstract

The unbalanced electromagnetic force and unqualified assembly of the spindle motor lead to weak vibration energy and the difference in fault features in different life cycles and different bearing individuals. Compared with single fault diagnosis and the datasets that come from the test bench, compound faults diagnosis of the spindle motor is a challenging task. To solve it, an improved filtering and feature enhancement method combined with the merits of AMF and TEO is proposed. Firstly, the adaptive morphological filtering (AMF) method is developed by adaptively constructing the size of the structural element (SE) for remaining in corresponding SE with the component of interest, which can reduce interference from background noise. Still, the periodic fault impulse, especially the incipient fault signals, is easily modulated by other fault-unrelated harmonic components. Thus, the filtered signals are processed by the Teager energy operator (TEO) for enhancing the faint transient impact compositions. Finally, the effectiveness of the method is verified by simulation and fault motor matched with NTN ceramic bearing and FAG metal bearing respectively, where the motor matched with NTN ceramic bearing is an early failure case and the other is a late failure case. Besides, compared with some traditional methods, the result proves that the proposed method has better performance under the actual engineering scenarios for different degrees of fault feature identification.