Study on Fault Diagnosis of Rolling Bearing Based on Time-Frequency Generalized Dimension

Abstract

The condition monitoring technology and fault diagnosis technology of mechanical equipment played an important role in the modern engineering. Rolling bearing is the most common component of mechanical equipment which sustains and transfers the load. Therefore, fault diagnosis of rolling bearings has great significance. Fractal theory provides an effective method to describe the complexity and irregularity of the vibration signals of rolling bearings. In this paper a novel multifractal fault diagnosis approach based on time-frequency domain signals was proposed. The method and numerical algorithm of Multi-fractal analysis in time-frequency domain were provided. According to grid typeJand order parameterqin algorithm, the value range ofJand the cut-off condition ofqwere optimized based on the effect on the dimension calculation. Simulation experiments demonstrated that the effective signal identification could be complete by multifractal method in time-frequency domain, which is related to the factors such as signal energy and distribution. And the further fault diagnosis experiments of bearings showed that the multifractal method in time-frequency domain can complete the fault diagnosis, such as the fault judgment and fault types. And the fault detection can be done in the early stage of fault. Therefore, the multifractal method in time-frequency domain used in fault diagnosis of bearing is a practicable method.

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

“…Based on cyclic spectral density, Dong et al [137] proposed a Wigner-Ville spectrum which is a noise resistant time frequency analysis technique for extracting bearing fault patterns. Yuan et al [138] used multi-fractal analysis in the time-frequency domain to identify fault types at an early stage. Siegel et al [139] proposed a tachometer-less synchronously averaged envelope feature extraction technique for rolling element bearing health assessment.…”

“…[126] Short-time Fourier-transform-based estimator of the spectral kurtosis Antoni J. [127] Fast computation of the kurtogram Li et al [132] Particle Filter + Kurtogram Wang et al [125] Minimum entropy de-convolution + Fast Kurtogram Cong et al [129] Spectral kurtosis + autoregressive model Jeong et al [130] Spectral kurtosis Chen et al [133] Mean envelope Kurtosis + envelope analysis Jia et al [131] Maximum correlated kurtosis deconvolution Masmoudi et al [134] Time synchronous averaging Dong et al [135] Frequency-shifted bispectrum Zhou et al [136] Cyclic bispectrum Dong et al [137] Wigner-Ville spectrum Yuan et al [138] Multi-fractal analysis Siegel et al [139] Tachometer-less synchronously averaged envelope Park et al [140] Minimum variance cepstrum Fu et al [141] Adaptive fuzzy-means clustering Li et al [142] Informative frequency band Liu et al [143] Adaptive SR + quantum particle swarm Liao et al [144] Improved genetic algorithm Kedadouche et al [124] Approximate entropy + sample entropy + Lempel-Ziv Complexity. Javorskyj et al [145] Periodically correlated random processes Igba et al [146] Root mean square (RMS) + peak values Shao et al [147] RMS in angle domain Sharma et al [148] Modified time synchronous averaging Jin et al [149] Mahalanobis distance…”

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

“…Vibration analysis has been widely applied to diagnose bearing faults. However, the faulty signal acquired from the bearing is usually weak or submerged in strong noise [3,4]. Traditional weak signal detection methods, such as empirical mode decomposition (EMD) [5], wavelets transform (WT) [6], singular value decomposition (SVD) [7], and variational mode decomposition (VMD) [8], mainly reduced noise to improve signal-to-noise ratio (SNR) and extract fault characteristics, which inevitably weakened useful fault signal characteristic information.…”

Study on Mechanical Concurrent Fault Diagnosis Method Based on PSO-MRLSSVM

“…Thus, the concurrent faults bring new challenges to fault diagnosis. Due to the close and cooperative relationship between equipment components, there is not a one-to-one correspondence between symptoms and causes for any fault; thus, the classification difficulty rises [3][4][5].…”