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Adaptively Detecting the Transient Feature of Faulty Wind Turbine Planetary Gearboxes by the Improved Kurtosis and Iterative Thresholding Algorithm

It is significant for diagnosing the early faults of wind turbine planetary gearboxes. However, the weak transient feature generated by the early gear failure is often submerged in other vibration signals and noise, thus this paper discusses a new automatic sparse representation method for detecting weak transients. An improved Morlet wavelet which strictly satisfies the admissibility condition is constructed, and it is highly beneficial to sparsely separating signal component. By the use of the improved Morlet wavelet dictionary and Fourier dictionary, the transient component can be extracted by solving a sparse problem. Aiming at the drawback of the kurtosis and squared envelope spectrum (SES) negentropy in evaluating the similarity between the extracted transient and the original transient, an improved kurtosis index is designed, which can be used to search the optimal sparse parameters. Then, by the use of the improved kurtosis index and iterative thresholding method, an adaptively iterative thresholding shrinkage algorithm is proposed to detect the repetitive transients from the planetary gearbox signal. The comparative results in simulation and experiments show that the proposed method can more effectively and accurately perform the fault diagnosis of wind turbine planetary gearboxes than the infogram method and the resonance-based signal decomposition method.