Fault Analysis of Wind Power Rolling Bearing Based on EMD Feature Extraction

Abstract

In a wind turbine, the rolling bearing is the critical component. However, it has a high failure rate. Therefore, the failure analysis and fault diagnosis of wind power rolling bearings are very important to ensure the high reliability and safety of wind power equipment. In this study, the failure form and the corresponding reason for the failure are discussed firstly. Then, the natural frequency and the characteristic frequency are analyzed. The Empirical Mode Decomposition (EMD) algorithm is used to extract the characteristics of the vibration signal of the rolling bearing. Moreover, the eigenmode function is obtained and then filtered by the kurtosis criterion. Consequently, the relationship between the actual fault frequency spectrum and the theoretical fault frequency can be obtained. Then the fault analysis is performed. To enhance the accuracy of fault diagnosis, based on the previous feature extraction and the time-frequency domain feature extraction of the data after EMD decomposition processing, four different classifiers are added to diagnose and classify the fault status of rolling bearings and compare them with four different classifiers.

A Dynamic Maintenance Strategy for Multi-Component Systems Using a Genetic Algorithm

“…It is desirable for a maintenance plan to be performed as accurately as possible to achieve optimal cost. In fact, the degradation state of systems changes dynamically, so it is difficult to perform accurate maintenance plans, especially for rotary structures [18][19][20]. In view of the disadvantages of the existing maintenance policies, a new condition-based maintenance (CBM) strategy is proposed based on PHM, which performs according to the analysis and prediction results of system degradation parameters.…”

An Uncertainty Analysis and Reliability-Based Multidisciplinary Design Optimization Method Using Fourth-Moment Saddlepoint Approximation

“…Therefore, it is necessary to use higher-order moments to improve the calculation accuracy. The use of the saddlepoint approximation (SA) for progressive analysis is efficient and practical [22][23][24][25][26][27][28].…”

Crack fault diagnosis of vibration exciter rolling bearing based on genetic algorithm–optimized Morlet wavelet filter and empirical mode decomposition

“…Tang et al 16 proposed a diagnosis method based on the Empirical Mode Decomposition (EMD) and envelope spectrum analysis to solve the problem that the fault characteristic signal is weak, and the traditional envelope analysis needs to rely on experience to determine the analysis frequency band in the early fault diagnosis of hydraulic pump. Meng et al 17 use an EMD algorithm to extract the characteristics of the rolling bearing vibration signals of wind turbines. F Liu et al 18 use the Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Linearly Decreasing Particle Swarm Optimization Probabilistic Neural Network (LDWPSO-PNN) methods to analyze and compare the vibration signals of rotating machinery.…”

“…Tang et al 16 proposed a diagnosis method based on the Empirical Mode Decomposition (EMD) and envelope spectrum analysis to solve the problem that the fault characteristic signal is weak, and the traditional envelope analysis needs to rely on experience to determine the analysis frequency band in the early fault diagnosis of hydraulic pump. Meng et al 17 method is further used to reprocess the signal filtered by the optimized Morlet wavelet filter.…”