Enhanced Bearing Fault Detection Using Step-Varying Vibrational Resonance Based on Duffing Oscillator Nonlinear System

Abstract

Bearing is a key part of rotary machines, and its working condition is critical in normal operation of rotary machines. Vibrational signals are usually analyzed to monitor the status of bearing. However, information on the status of bearing is always buried in heavy background noise; that is, status information of bearing is weaker than the background noise. Extracting the status features of bearing from signals buried in noise is difficult. Given this, a step-varying vibrational resonance (SVVR) method based on Duffing oscillator nonlinear system is proposed to enhance the weak status feature of bearing by tuning different parameters. Extraction ability of SVVR was verified by analyzing simulation signal and practical bearing signal. Experimental results show that SVVR is more effective in extracting weak characteristic information than other methods, including multiscale noise tuning stochastic resonance (SR), Woods-Saxon potential-based SR, and joint Woods-Saxon and Gaussian potential-based SR. Two evaluation indices are investigated to qualitatively and quantitatively assess the fault detection capability of the SVVR method. The results show that the SVVR can effectively identify the weak status information of bearing.

Chaos-based signal detection with discrete-time processing of the Duffing attractor

“…One of the most important trends in development of chaos-based signal detection methods is communication where chaotic systems are used as highly sensitive input circuits of receivers [6]. Besides, high sensitivity of chaotic systems is used to diagnose mechanical faults [18] for the purpose of non-destructive testing. Application of a chaotic system to seismic signal processing was described in [19].…”

“…There is also an example of use of chaotic mapping in analysis of Internet traffic in order to detect LDoS attacks [20]. Studies [6,[18][19][20] also used a detection method using transition to the periodic state which is a significant drawback. Insufficient practical implementation of these methods is associated with the difficulty of maintaining a stable threshold of signal detection and the need to solve the problem of chaos identification.…”

“…Thus, the desired transformation (7) which describes the signal detection process has the form of expression (18):…”

A New Second-Order Tristable Stochastic Resonance Method for Fault Diagnosis

“…Xia [26] proposed an improved SR method with an arbitrary stable-state potential for incipient bearing fault diagnosis. Liu [27] proposed a step-varying vibrational resonance (SVVR) method based on the duffing oscillator nonlinear system and concluded that the SVVR is more effective in extracting weak characteristic information than other methods. All in all, the second-order SR method is able to obtain higher output SNR and better performance than the first-order SR method.…”

The Method of Quantitative Trend Diagnosis of Rolling Bearing Fault Based on Protrugram and Lempel–Ziv

“…However, in practical engineering, the signal features are very weak and vulnerable to the influence of strong background noise [13,14], which is a typical nonstationary signal [15]. At this point, not all frequency bands in the signal have obvious fault characteristics.…”