Weak signal enhancement for rolling bearing fault diagnosis based on adaptive optimized VMD and SR under strong noise background

Abstract

Owing to the nonlinearity and nonstationarity of the bearing fault signal, it is difficult to identify fault characteristics under the influence of a strong noise environment. The extraction of early weak fault features is critical for the reliability of bearing operations. Therefore, an urgent problem is reasonable noise reduction and feature enhancement in weak-signal processing. Traditional variational modal decomposition (VMD) and stochastic resonance (SR) are commonly applied to detect weak signals in fault diagnosis. The VMD method can decompose the signal into several intrinsic mode functions (IMFs) to effectively reduce the modal aliasing problem. However, uniform standards for the key parameters of decomposition and the selection of the optimal IMF after decomposition are lacking. Meanwhile, some disadvantages of SR still exist; for example, the interference of multiscale noise may lead to false detection by incorrect selection of high-pass filter parameters, and the system parameters are not adaptive to different signals to achieve the best response output. To better address the weak signal feature enhancement, a novel rolling bearing fault diagnosis method combining adaptive VMD and SR by improved differential search (IDS) optimization is proposed. First, the bearing fault signal is decomposed into several IMFs using the IDS-VMD. Second, the feature information of the fault signal is retained and reconstructed using the correlation kurtosis for sensitive modal extraction. Furthermore, the fault features of the reconstructed signal are effectively enhanced by the variable-step IDS-SR, which can reasonably transfer the noise energy of the input components to the fault characteristic frequency. Finally, the periodic pulse can be observed in the corresponding envelope spectrum. The simulated and experimental data show that the proposed method can not only effectively extract the signal feature information in the actual fault but also realize early weak fault diagnosis of rolling bearings more accurately.

Low-rank and periodic group sparse based signal denoising method for rolling bearing fault feature extraction

“…Proof 1. To prove that equation ( 9) is a convex function, it is divided into two parts: 1 2 ∥Y − X∥…”

“…In [16], Wang et al proved that ∥X∥ T is also a convex function. Therefore, according to the principle that the sum of two convex functions is still a convex function [34], we can obtain that 1 2 ∥Y − X∥…”

“…where S is a random integer in [1,10]. f n = 2000 Hz, φ n ∼ N (0, 1), and each failure impulse contains 20 points.…”

“…Rolling bearing fault diagnosis is a crucial step to assure smooth operation of mechanical equipment and avoid mechanical accidents. This is because rolling bearings are applied in numerous mechanical equipment and are prone to local failures when operating in harsh environments, which can severely impair the performance of machinery and even induce catastrophic accidents [1][2][3][4]. Rolling bearings with After decades of research by scholars, numerous rolling bearing failure detection approaches on the basis of vibration signal analysis have been proposed, such as the wavelet transform [10,11], variational modal decomposition [12,13], sparse representation [14,15] and sparse low-rank (LR) (SLR) [16,17].…”

Study on the optimization of the classification method of rolling bearing fault type and damage degree based on SFO–VMD

“…In 2022, Gu et al [18] proposed a feature extraction method that combines the VMD method and permutation entropy (PE). To address the problem of weak signal feature enhancement, Luo et al [19] presented an adaptive VMD method using improved difference search.…”