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In this study, the failure of a certain type of gearbox of a high-speed locomotive group with cracks is examined, and a gearbox failure assessment method that considers the coupled vibration is established and combined with mechanical signal and frequency signal to determine the basis for judging the failure of a faulty gearbox. First, according to the mechanical model of the finite element calculation, we determined the stress weak links and then the layout response stress measurement points and acceleration measurement points. We then calculated the gearbox ratio, meshing frequency, vibration frequency, mechanical response, modal response, and other frequency characteristics using the Hilbert–Huang transform (HHT) method and the fast Fourier transform (FFT) algorithm to analyze the vibration signals generated by different speeds and wheel out-of-roundness conditions. These were used to calculate the frequency of the different vibration sources of the mechanical response on the weak areas. The frequency correlations of the different vibration sources on the mechanical response in the weak areas were then analyzed, and the vibration transmission law of the gearbox case was obtained. The fault determination criterion was then determined, and the final cause of the fault was obtained.

Maintenance management in wind energy industry has great impact on the overall wind power cost. Maintenance services are either supported by wind turbine manufacturers within warranty period, or managed by wind farm owners. With condition-based maintenance (CBM) strategy, maintenance activities are scheduled based on the predicted health conditions of wind turbine components, and accurate prognostics methods are critical for effective CBM. The reported studies on integrated health prognostics considered the uncertainty in crack initiation time (CIT) uncertainty, but did not incorporate time-varying loading conditions, which could also have a significant impact on future health condition and remaining useful life (RUL) prediction. Constant loads were generally used to approximate the actual time-varying loading conditions. In this paper, an integrated prognostics method is proposed for wind turbine gearboxes considering both time-varying loading conditions and CIT uncertainty. As new condition monitoring observations are available, the distributions of both material model parameter and CIT are updated via Bayesian inference, and the failure time prediction is updated accordingly. An example is provided to demonstrate that the proposed time-varying load approach presents more benefits considering the uncertainty of CIT, with significant accuracy improvement comparing to the constant-load approach.

The paper introduces the concept of exploring the potential of Ensemble Empirical Mode Decomposition (EEMD) and Sparsity Measurement (SM) in enhancing the diagnostic information contained in the Time Synchronous Averaging (TSA) method used in the field of gearbox diagnostics. EEMD was created as a natural improvement of the Empirical Mode Decomposition which suffered from a so-called mode mixing problem. SM is heavily used in the field of ultrasound signal processing as a tool for assessing the degree of sparsity of a signal. A novel process of automatically finding the optimal parameters of EEMD is proposed by incorporating a Form Factor parameter, known from the field of electrical engineering. All these elements are combined and applied on a set of vibration data generated on a 2-stage gearbox under healthy and faulty conditions. The results suggest that combining these methods may increase the robustness of the condition monitoring routine, when compared to the standard TSA used alone.

In order to overcome the disadvantage of fractal dimension such as box dimension and correlation dimension in fault diagnosis, a method of fault diagnosis based on fractal scale is studied. An improved algorithm of local fractal scale is presented. Non-stationary characteristic of vibration signal and its local details are revealed and measured quantificationally. The running condition of equipment is monitored by global fractal scale, fault causation is analyzed by local fractal scale. Successful application has been achieved to detect abrasion fault of gearbox. The results show that the fractal scale is more sensitive to abrasion fault of gearbox than box dimension, the method of fault diagnosis by using fractal scale based on wavelet transform can provide a new effective technology for fault diagnosis.

In order to overcome the disadvantage of traditional methods of fault features extraction, and to realize the on-line and intelligent fault diagnosis, a new method of feature extraction based on the lifting wavelet packet transform is presented, with which fault feature factors were extracted form Four typical running states of gearbox. The results show that the method of fault feature extraction based on the lifting WPT can reduce the need of time and memory greatly, and it is very fit to the real-time fault diagnosis system.

Studying the characteristics of gear meshing is significant for improving the transmission performance of a gearbox. According to the structure of the off-road vehicle gearbox, the dynamics virtual prototype model of the gearbox transmission system was established by ADAMS. Based on Hertz contact theory, the dynamics simulation of gear meshing force was conducted with consideration of the influence of manufacturing and assembly errors. The results indicate that controlling the manufacturing and assembly errors can effectively reduce gear noise and gear wear, which improves the working performance of the gearbox.

In the gearbox fault diagnosis, the signal collected by sensors is generally suffered by the disturbance from various types of unknown noise. Under the complex noise environment, the blind source separation of the gearbox fault cannot obtain perfect result of separation. In order to solve this problem, a new noisy blind source separation method of gearbox fault based on particle filter is proposed. A denoising processing to the observation signal is implemented using Rao-blackwellised particle filter before the independent component analysis. The simulation and experimental results show that the proposed method is effective.