State assessment for bearing rotor static unbalance based on Welch-PSD and SAE

Abstract

Bearings are an important part of mechanical equipment and it will cause a series of mechanical failures once the malfunction of bearing occurs. Rotor unbalance is the most common type of bearing failure; thus the assessment of bearing rotor unbalance is essential to maintain the normal operation of mechanical. In this paper, a method based on Welch power spectral density estimate (Welch-PSD) and stacked automatic encoder (SAE) is proposed to achieve state assessment of bearing rotor static unbalance by processing the two-way vibration signals collected by the acceleration sensor installed in the vertical and horizontal directions of the bearing. Firstly, the Welch-PSD method is used to decompose the vibration signal to obtain the power spectral density, and the vibration power of the working frequency is taken as the feature. Then, the Stacked Auto-Encoder method is introduced to assessment the bearing rotor unbalance state. This paper designs an experiment of rotor unbalance fault in different degree to verify the accuracy of the designed method. The experimental results show that the Welch-PSD method can accurately extract the rotor unbalance fault feature. In addition, the SAE neural network can apply the fault feature to accurately assessment the bearing rotor unbalance degree.