A New Method Based on Time-Varying Filtering Intrinsic Time-Scale Decomposition and General Refined Composite Multiscale Sample Entropy for Rolling-Bearing Feature Extraction

Abstract

The early fault diagnosis of rolling bearings has always been a difficult problem due to the interference of strong noise. This paper proposes a new method of early fault diagnosis for rolling bearings with entropy participation. First, a new signal decomposition method is proposed in this paper: intrinsic time-scale decomposition based on time-varying filtering. It is introduced into the framework of complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN). Compared with traditional intrinsic time-scale decomposition, intrinsic time-scale decomposition based on time-varying filtering can improve frequency-separation performance. It has strong robustness in the presence of noise interference. However, decomposition parameters (the bandwidth threshold and B-spline order) have significant impacts on the decomposition results of this method, and they need to be artificially set. Aiming to address this problem, this paper proposes rolling-bearing fault diagnosis optimization based on an improved coyote optimization algorithm (COA). First, the minimal generalized refined composite multiscale sample entropy parameter was used as the objective function. Through the improved COA algorithm, optimal intrinsic time-scale decomposition parameters based on time-varying filtering that match the input signal are obtained. By analyzing generalized refined composite multiscale sample entropy (GRCMSE), whether the mode component is dominated by the fault signal is determined. The signal is reconstructed and decomposed again. Finally, the mode component with the highest energy in the central frequency band is selected for envelope spectrum variation for fault diagnosis. Lastly, simulated and experimental signals were used to verify the effectiveness of the proposed method.

Adaptive Stochastic Resonance-Based Processing of Weak Magnetic Slippage Signals of Bearings

“…The minimum value is found with the cuckoo search algorithm, while the maximization of the signal-to-noise proportion of the output signal is the metric of the detection effect of the weak magnetic signal in the proposed method. Therefore, to reduce the interference of noise on the signal results, we selected the improved sample entropy [ 42 ] as the objective function, normalized the two, and took the reciprocal of its normalized result as the seeking target of the cuckoo search algorithm. For each search agent, the signal-to-noise proportion of the SR output signal was calculated on basis of the literature [ 24 ].…”

“…This method was also used by the authors of [24]. We considered the problem of excessive noise intensity of weak magnetic signals and used improved sample entropy [42] as one of the parameters to evaluate the output results. The specific implementation of the method was as below (1) Input the analysis signal and assign the ATSR system parameters to search range and Enhanced MFO algorithm parameters.…”

Research on early fault feature extraction technology of aviation bearing based on noise estimation ITD

“…Ren and collaborators [20] developed an ITD-PE-SVD method for feature extraction. Several amendments were proposed for the ITD method by Ma and colleagues [21][22][23][24] Extensive research on the VDM algorithm and its improved SVDM algorithm was conducted by Li et al [25,26].…”

Fault Feature-Extraction Method of Aviation Bearing Based on Maximum Correlation Re’nyi Entropy and Phase-Space Reconstruction Technology

“…The failure of rolling bearings, as a common component of aeroengines, seriously affects the aircraft flight safety. Hence, the early fault diagnosis of key aeroengine components is of great significance to ensuring flight safety and lowering economic and life losses [ 1 ].…”