Distinguish coherence resonance and stochastic resonance in bearing fault evaluation

Noise-assisted stochastic resonance (SR) method has been extensively applied in bearing fault diagnosis. In order to cast off the basic constraint on nonlinearity in classical SR systems, and overturn the passive energy conversion from external noise to signal, a fluctuating-frequency linear oscillator (FFLO) is proposed and combined with the generalized scale transformation (GST) to overcome the small parameter limitation in this study. The results of output power amplification reveal that the proposed GST-FFLO system displays a rich variety of generalized SR (GSR) behaviors, which play an active role in the optimal energy conversion from internal regulatable noise to weak bearing fault signal. Moreover, it is also found that the undamped GST-FFLO system always produces more significant GSR peaks, thereby improving the precision and efficiency in the energy conversion. Finally, the experimental results demonstrate that the proposed method is always valid and exhibits the superiority in diagnosis performance and operating efficiency in several typical difficult cases, namely, impulse interference, low signal-to-noise ratio, and multiple faults.

Section: Gst-fflo Systemmentioning

confidence: 99%

Section: Simulated Diagnosismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bearing fault diagnosis based on the active energy conversion of generalized stochastic resonance in fluctuating-frequency linear oscillator

Wang

Chen

Liu

2021

“…In the past decade, a broad spectrum of SR methods with arbitrary frequency parameters has been developed for the fault diagnosis of mechanical systems, such as parameter tuning [21], rescaling frequency [22], variable step size [23], frequency shift, rescaling [24], and multi-scale noise tuning [25]. Therefore, the SR method has been widely applied in the field of bearing fault information extraction [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Bearing fault feature extraction method: stochastic resonance-based negative entropy of square envelope spectrum

Zhao,

Jiang,

Wang

et al. 2024

The early identification of bearing defects has recently attracted increasing attention in the fields of condition monitoring and predictive maintenance because of the critical role of bearings on the reliability and safety of turbomachines. The weak features representing early faults in the vibration signals are often submerged in the environmental noise, which poses a major challenge for the early fault diagnosis of rolling bearings. This study proposes a negative entropy of the square envelope spectrum approach integrated with optimized stochastic resonance-based signal enhancement for accurate early defect detection of rolling element bearings. The proposed method considers the cyclostationarity and impulsivity of the raw signal, as well as its similarity with the enhanced signal, thus reinforcing the characteristic frequency while integrating the regularity of the raw signal to evaluate the stochastic resonance performance. A comparison study with different existing methods using both numerical and experimental data was conducted to illustrate the effectiveness and accuracy of the proposed methodology for early defect detection of rolling element bearings in different locations. The results show that the proposed method improves the fault detection by 3.5 days earlier than other stochastic resonance methods, and produces the best enhancement results for fault detection in the outer race, inner race, and rolling element of bearings, with the increase of characteristic frequency intensity coefficient by 126.3%, 118.1%, and 100.5% compared to traditional envelope signals, respectively.

“…The SR system has been an attractive topic in the field of weak signal detection, especially early mechanical fault diagnosis. Yang et al [10] introduced the concept of coherent resonance (CR), an ordered dynamic behavior similar to SR, and by distinguishing CR and SR, a bearing fault can be diagnosed successfully. Zhang et al [11] proposed a piecewise unsaturated asymmetric tristable SR system on early bearing fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Adaptive progressive learning stochastic resonance for weak signal detection

zong¹,

Men²,

An³

et al. 2023

Stochastic resonance (SR) can enhance signals by using noise. This has attracted more attention in the field of weak signal detection. In practical applications, owing to the non-adjustability of noisy signals, SR is required to adjust the system parameters adaptively to satisfy the conditions of the SR phenomenon. In this paper, an adaptive progressive learning stochastic resonance (APLSR) method is proposed to improve the detection ability of weak signal, and the SR phenomenon is quantitatively defined. A theoretical learning framework is established with an improved reinforcement learning model by mapping the nonlinear system parameter space to a progressive learning set. By selecting a proper learning layer within a determined constraint range, the matching system parameters can be quickly and accurately searched to generate a desired optimal output. Numerical simulation results show that the signal energy and the output-SNR can be enhanced significantly, which reflects an excellent weak signal detection performance especially for low signal-to-noise ratio (SNR) conditions. Finally, a diagnosis on the outer race fault signals of rolling bearing confirms that the proposed method can effectively detect fault characteristics.