Wind Turbine Bearing Incipient Fault Diagnosis Based on Adaptive Exponential Wavelet Threshold Function With Improved CPSO

Abstract

Signal processing is of vital importance to the incipient fault diagnosis and the safety running of wind turbines. To adaptively eliminate noise and retain the underlying fault characteristic signal, an adaptive exponential wavelet threshold denoising method based on chaotic dynamic weight particle swarm optimization with sigmoid-based acceleration coefficients (SBAC-CDWPSO) is proposed in this paper. Firstly, a high-order continuous differentiable adaptive exponential threshold function (AETF) based on stein unbiased risk estimation is put forward to improve the defects of the traditional threshold functions. Secondly, the sine map and the sigmoid-based acceleration coefficients are applied to the velocity updating mechanism in particle swarm optimization (PSO). Meanwhile, the dynamic weight, the acceleration coefficient and the best-so-far position are introduced to update the new position with the previous position and the velocity in PSO. And the gaussian mutation strategy is added, which can effectively maintain the diversity of the swarm and get rid of local optimization. Thirdly, the SBAC-CDWPSO is used to optimize the threshold iteration process in AETF, which can greatly improve the iteration speed of the optimal threshold, and enhance the noise reduction effect. Experimental results showed that the signal-to-noise ratio of our proposed method was higher and the root mean square error was smaller comparing with the preexisting algorithms. Moreover, wind turbine generator bearing fault diagnosis classification results illustrated that the fault diagnosis rate of the proposed denoising algorithm was up to 96.67%, indicating that the proposed method has great potential in the incipient fault diagnosis of wind turbine bearings.INDEX TERMS Adaptive wavelet threshold, dynamic weight particle swarm optimization, fault diagnosis, sigmoid-based acceleration coefficients, wind turbine bearing.

A Probabilistic Bayesian Parallel Deep Learning Framework for Wind Turbine Bearing Fault Diagnosis

“…Comparing the literature [ 13 , 25 ] and traditional machine learning fault diagnosis methods with optimal BayesianPDL, the input signal of different diagnostic methods is the raw vibration signal. Among them, the PSO-SVM method and the CMCPSO-SVM method were obtained through the literature 13.…”

Shearer Reliability Prediction Using Support Vector Machine Based on Chaotic Particle Swarm Optimization Algorithm

“…If the PSO falls into the local extremum, the velocities of all particles are easy to rapidly decrease to zero and stop flying, which causes the premature convergence [26]. Howerver, characterized as ergodicity, randomicity and regularity [27], the chaotic search can experience all positions in a specific area without repeat. So the chaos idea is introduced to PSO algorithm (CPSO) to enhance the performance of PSO.…”

Fractional order total variational infrared image denoising with fused flower pollination particle swarm optimization

“…Zhang et al 32 proposed a dual adaptive denoising algorithm based on complementary integrated empirical modal decomposition and PSO to improve the signal-to-noise ratio of the Brillouin optical time-domain analysis sensor. Kong et al 33 proposed a chaotic dynamic weighted particle swarm adaptive wavelet threshold denoising method based on sigmoid acceleration coefficients to adaptively eliminate noise and retain the underlying fault characteristic signal of wind turbines.…”