Fault severity assessment of rolling element bearings based on bicoherence spectrum

The condition monitoring and fault diagnosis of rolling element bearings not only depend on fault type but also fault severity which is also essential for making proper maintenance decision. Aiming at fault severity assessment of rolling element bearings, a new indicator based on the bicoherence spectrum is proposed. The quadratic phase coupling (QPC) which is one of the most important nonlinear characteristics of fault-related signals can be detected by the bicoherence spectrum. The deepening fault severity usually results in more frequency components involving in coupling. In order to quantify the coupling degree which is closely related to fault severity, we choose the mean value of the bicoherence spectrum as an indicator. Experiments results showed that the quantitative trend prediction of different fault severities of bearing is realized

A Wavelet Bicoherence-Based Quadratic Nonlinearity Feature for Translational Axis Condition Monitoring

The translational axis is one of the most important subsystems in modern machine tools, as its degradation may result in the loss of the product qualification and lower the control precision. Condition-based maintenance (CBM) has been considered as one of the advanced maintenance schemes to achieve effective, reliable and cost-effective operation of machine systems, however, current vibration-based maintenance schemes cannot be employed directly in the translational axis system, due to its complex structure and the inefficiency of commonly used condition monitoring features. In this paper, a wavelet bicoherence-based quadratic nonlinearity feature is proposed for translational axis condition monitoring by using the torque signature of the drive servomotor. Firstly, the quadratic nonlinearity of the servomotor torque signature is discussed, and then, a biphase randomization wavelet bicoherence is introduced for its quadratic nonlinear detection. On this basis, a quadratic nonlinearity feature is proposed for condition monitoring of the translational axis. The properties of the proposed quadratic nonlinearity feature are investigated by simulations. Subsequently, this feature is applied to the real-world servomotor torque data collected from the X-axis on a high precision vertical machining centre. All the results show that the performance of the proposed feature is much better than that of original condition monitoring features