Wind turbine gearboxes operate in harsh environments; therefore, the resulting gear vibration signal has characteristics of strong nonlinearity, is non-stationary, and has a low signal-to-noise ratio, which indicates that it is difficult to identify wind turbine gearbox faults effectively by the traditional methods. To solve this problem, this paper proposes a new fault diagnosis method for wind turbine gearboxes based on generalized composite multiscale Lempel–Ziv complexity (GCMLZC). Within the proposed method, an effective technique named multiscale morphological-hat convolution operator (MHCO) is firstly presented to remove the noise interference information of the original gear vibration signal. Then, the GCMLZC of the filtered signal was calculated to extract gear fault features. Finally, the extracted fault features were input into softmax classifier for automatically identifying different health conditions of wind turbine gearboxes. The effectiveness of the proposed method was validated by the experimental and engineering data analysis. The results of the analysis indicate that the proposed method can identify accurately different gear health conditions. Moreover, the identification accuracy of the proposed method is higher than that of traditional multiscale Lempel–Ziv complexity (MLZC) and several representative multiscale entropies (e.g., multiscale dispersion entropy (MDE), multiscale permutation entropy (MPE) and multiscale sample entropy (MSE)).

Application of Generalized Composite Multiscale Lempel-Ziv Complexity in Identifying Wind Turbine Gearbox Faults.

The gearbox is an important component in the mechanical transmission system and plays a key role in aerospace, wind power and other fields. Gear failure is one of the main causes of gearbox failure, and therefore it is very important to accurately diagnose the type of gear failure under different operating conditions. Aiming at the problem that it is difficult to effectively identify the fault types of gears using traditional methods under complex and changeable working conditions, a fault diagnosis method based on multi-sensor information fusion and Visual Geometry Group (VGG) is proposed. First, the power spectral density is calculated with the raw frequency domain signal collected by multiple sensors before being transformed into a power spectral density energy map after information fusion. Second, the obtained energy map is combined with VGG to obtain the fault diagnosis model of the gear. Finally, two datasets are used to verify the effectiveness and generalization ability of the method. The experimental results show that the accuracy of the method can reach 100% at most on both datasets.

/pdf/gear-fault-diagnosis-method-based-on-multi-sensor-3sn505n3.pdf

Gear Fault Diagnosis Method Based on Multi-Sensor Information Fusion and VGG

The signal noise can have a negative impact on the accuracy of engineering equipment condition assessment. However, the choice of decomposition layers and design parameters significantly impacts the outcomes of spectral graph wavelet transform (SGWT) methods. Conventional SGWT techniques often rely on predefined design parameters and decomposition layers, which can limit satisfactory decomposition results. To address this challenge, we present a novel parameter-adaptive SGWT method, referred to as PASGWT, designed for denoising vibration signals from wind turbines (WTs). PASGWT adaptively adjusts the optimal design parameters and decomposition layers to align with the signal characteristics. Furthermore, it introduces a novel evaluation metric to effectively identify fault-related features in signals corrupted by strong noise interference. Initially, the vibration signal is transformed into a directed graph representation. Subsequently, an SGWT with a warping function is constructed to process the signal. To guide the optimization process, we introduce a comprehensive evaluation index called the combination kurtosis index (CKI), which integrates periodic kurtosis and envelope spectrum kurtosis. The SGWT parameters are then optimized by the Hunter-Prey optimization (HPO) algorithm with maximum CKI value as the optimization objective. Finally, the denoised signal is reconstructed by selecting components with CKI values exceeding the average CKI value. The efficacy and practicality of the proposed method are validated through case studies involving simulated signals and two real-world fault signals from a scaled-down wind turbine test rig. Furthermore, comparative experiments highlight the superiority of the proposed method. 

A Parameter-Adaptive Spectral Graph Wavelet Transform Method for Wind Turbines Vibration Signal Denoising

The signal generated by a diesel engine is considered as relatively intricate mechanical signal due to its susceptibility to interference from both low-pass components and significant background noise. In order to improve the feature extraction performance, a novel method termed low-pass filtering (LPF)/overlapping group shrinkage (OGS) (the simultaneously LPF and OGS denoising method) is proposed. For filtering low-frequency components, a zero-phase noncausal recursive filter that takes the form of a banded matrix is employed. Meanwhile, the nonconvex regularization technique, which incorporates the group sparse structure is adopted to capture the tendency of clustering. Moreover, the whole formulated objective function is constrained as a convex optimization problem. As a result, the sparsity of useful features is maximally promoted, while the noise between adjacent pulses is eliminated. The proposed method is applied to a simulated signal and a real signal collected from the diesel engine. The processing results provide compelling evidence that the proposed method outperforms traditional methods in accurately extracting transient impulses for machinery fault diagnosis.

LPF/OGS: A low-pass filtering and overlapping group shrinkage denoising method for diesel engine fault diagnosis

Due to the high accuracy achieved in data-driven fault diagnosis, time-frequency images generated by Continuous Wavelet Transform (CWT) are widely used as the input of deep learning methods. However, the image data require huge amount of data memories. An adaptive resize technique provides a reliable way for deducing the scale of image and achieving good effect. In this study, a novel Bayesian adaptive resize-residual network was proposed to resize the input data scale and extract the image feature for mechanical fault diagnosis. The CWT and Histogram Equalization (HE) algorithm were used to generate enhanced time-frequency images. The newly developed adaptive resize-residual network was applied for feature extraction, in which the adaptive resize block can adaptively resize input image by self-learning, and the residual block was used for classification. The Bayesian optimization was introduced to optimize the model hyper parameters and obtain an effective model. A testbeds of rolling element bearings are introduced to support the experiments. The experimental results indicate that the proposed Bayesian adaptive resize-residual network obtains superior recognition accuracy and outperforms many state-of-the-art methods. This method is conducive to improving the capabilities of rotating machinery fault diagnosis, and reduces the repair time of fault. 

A Bayesian Adaptive Resize-Residual Deep Learning Network for Fault Diagnosis of Rotating Machinery

Fault diagnosis of wind turbines is of great importance to reduce operating and maintenance costs of wind farms. At present, most wind turbine fault diagnosis methods are focused on single faults, and the methods for combined faults usually depend on inefficient manual analysis. Filling the gap, this paper proposes a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbines simply and efficiently without human experience and with low computation costs. In this method, low-pass filtering empirical wavelet transform is proposed to extract fault features from vibration signals, LPFEWT energies are selected to be the inputs of the fault diagnosis model, a grey wolf optimizer hyperparameter tuned support vector machine (SVM) is employed for fault diagnosis. The method is verified on a wind turbine test rig that can simulate shaft misalignment and broken gear tooth faulty conditions. Compared with other models, the proposed model has superiority for this classification problem.

https://www.mdpi.com/1099-4300/23/8/975/pdf

Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines.

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Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines.