Early weak fault diagnosis of rolling element bearing based on resonance sparse decomposition and multi-objective information frequency band selection method

Abstract

Vibration signals of rolling element bearing’s early weak fault are often submerged by some interference components. To extract early weak fault features accurately, a weak fault feature enhancement method of rolling element bearing based on resonance sparse decompositionand multi-objective information frequency band selection is proposed. This method makes full use of resonance sparse decomposition in filtering the interferences and multi-objective information frequency band selection in enhancing impulsive and cyclostationary features of rolling element bearing’s early weak fault simultaneously. First, resonance sparse decomposition is used as the preprocessing program of multi-objective information frequency band selection to filter the interferences (such as rotating frequency with its harmonics) of rolling element bearing’s early weak fault characteristic components. Then, the filtered components containing main fault information are analyzed by MIFBS to establish the best band-pass filter. Finally, the envelope spectrum analysis is applied on the filtered vibration signal, and the fault characteristic frequency with its harmonics is extracted. To achieve the optimal output parameters of multi-objective information frequency band selection, fusion indexes based on time- and frequency-domain estimators are proposed and used to balance the enhancement of impulsive and cyclostationary characteristics of rolling element bearing’s early weak fault signals. Compared with most of the existing methods mainly based on single time-domain estimators or frequency-domain estimators to improve performance spectral kurtosis, the proposed fusion indexes overcome their defects and could enhance the impulsive and cyclostationary features of rolling element bearing’s early weak fault simultaneously. Effectiveness of the proposed method is verified through simulation and experiment. Besides, its advantage over the other related methods is also presented.

Automatic Fault Diagnosis Technology of Roller Bearings of High-speed Rail Based on IFD and AE

“…Xiao X et al constructed a framework for bearing fault diagnosis using a knowledge graph and data accumulation strategy to ensure the safe operation of high-speed rail roller bearings, effectively improving the diagnostic prediction accuracy and robustness of bearings based on the IFD method [8]. Wang H et al extracted the early faint fault characteristics of rolling bearings accurately in the IFD method and frequency band method of multi-target information; they proposed an early fault feature enhancement method for rolling bearings, which effectively improved the accuracy of fault diagnosis and ensured the smooth operation of high-speed trains [9]. Gong T et al proposed a new extraction method based on the adaptive stochastic IFD method to address the difficulty of extracting nonsmooth information of bearing faults under a robust noise environment so as that The feature information of the bearing is effectively enhanced based on the conversion of nonsmooth information into smooth details [10].…”

Vibration analysis of shaft-cylindrical roller-bearing-pedestal system considering damage by Runge–Kutta algorithm

“…During operation, the bearing will be slightly damaged under the action of mechanical load, lubrication deterioration, slip, and other factors, and the damage degree will further increase with the increase of operation time (Liu et al, 2021; Wang and Du, 2022; Li et al, 2021). The vibration signal measurement and performance prediction of the shaft-cylindrical roller-bearing-pedestal system considering damage can effectively ensure the safe operation of the system.…”

Sparsity assisted intelligent recognition method for vibration-based machinery health diagnostics

“…On the one hand, by the merits of morphological component analysis, sparsity-based signal processing methods were used to consider distinct oscillation behavior of multi-components and enhance the fault-induced feature for health diagnostics. Sparse decomposition models using dual tunable Q-factor wavelet transforms were studied for gear and bearing health diagnostics (Cai et al, 2013; Wang and Du, 2021). Du et al (2015) studied a sparse optimization model considering the union of redundant dictionaries (i.e., harmonic dictionary and Gabor dictionary) for gearbox health diagnostics.…”