Fault Diagnosis Of Wind Turbine Gearbox Research Articles

Fault diagnosis of gearboxin wind turbine based on EMD-DCGAN

INTRODUCTION: Wind turbine gearbox fault diagnosis is of great significance for the safe and stable operation of wind turbines. The accuracy of wind turbine gearbox fault diagnosis can be effectively improved by using complete wind turbine gearbox fault data and efficient fault diagnosis algorithms.A wind turbine gearbox fault diagnosis method based on EMD-DCGAN method is proposed in this paper.
 OBJECTIVES: It can solve the problem when the sensor fails or the data transmission fails, it will lead to errors in the wind turbine gearbox fault data, which in turn will lead to a decrease in the wind turbine gearbox fault diagnosis accuracy.
 METHODS: Firstly, the outliers in the sample data need to be detected and removed. In this paper, the EMD method is used to eliminate outliers in the wind turbine gearbox fault data samples with the aim of enhancing the true continuity of the samples; secondly, in order to make up for the lack of missing samples, a data enhancement algorithm based on a GAN network is proposed in the paper, which is able to effectively perfect the missing items of the sample data; lastly, in order to improve the accuracy of wind turbine gearbox faults, a DCGAN neural network-based fault diagnosis method is proposed, which effectively combines the data dimensionality reduction feature of deep learning method and the data enhancement feature of generative adversarial network, and can improve the accuracy and speed of fault diagnosis.
 RESULTS and CONCLUSIONS: The experimental results show that the proposed method can effectively identify wind turbine gearbox fault conditions, and verify the effectiveness of the algorithm under different sample data conditions.

An Envelope Time Synchronous Averaging for Wind Turbine Gearbox Fault Diagnosis

An Envelope Time Synchronous Averaging for Wind Turbine Gearbox Fault Diagnosis

An enhanced instantaneous angular speed estimation method by multi-harmonic time–frequency realignment for wind turbine gearbox fault diagnosis

Gearboxes are one of the most critical transmission devices in wind turbines (WTs). Order tracking (OT) of variable-speed WT gear is a powerful means to carry out fault diagnosis. Since it is difficult to install a tachometer or encoder for gearboxes with limited structural space, it is impossible to obtain the instantaneous angular speed (IAS) of the gearbox. In recent years, many tacholess techniques have been presented to estimate instantaneous phase from vibration signals for OT. However, their performance needs to be further improved to get a more exact diagnosis result. To solve this issue, this paper presents an enhanced IAS estimation approach for WT tacholess order tracking (TLOT), which is available for accurate IAS estimation of the rotating shaft, so that TLOT can be more effectively conducted under conditions of large speed variation and background noise. An improved cost function algorithm is designed to extract the IAS from the vibration signal. A Vold–Kalman filter and Hilbert transform are combined to calculate the instantaneous phase. Afterwards, the raw non-stationary vibration signal is resampled at an equal angle increment, and the envelope order spectrum is calculated from the resampled signal for fault diagnosis of the WT gearbox. The main innovating idea of this approach is to improve the traditional cost function ridge-extraction method by introducing gradient information based on probability and multi-harmonic time–frequency realignment, which makes the IAS estimation process more accurate and overcomes the limitations of traditional IAS estimation techniques. The effectiveness of the presented approach is demonstrated by using a vibration signal measured from real-world WT gearboxes, and the validation results demonstrate that the presented approach surpasses some state-of-the-art methods in IAS estimation accuracy and efficiency.

Wind Turbine Gearbox Fault Diagnosis Using Cyclostationary Analysis and Interpretable CNN

Wind Turbine Gearbox Fault Diagnosis Using Cyclostationary Analysis and Interpretable CNN

Semisupervised Momentum Prototype Network for Gearbox Fault Diagnosis Under Limited Labeled Samples

It is difficult to obtain expensive labeled data in industrial fault diagnosis applications, which easily leads to overfitting of deep learning and restricts its extensive usage. Aiming at this issue, this article proposed an improved few-shot semisupervised learning method, called semisupervised momentum prototype network (SSMPN), to realize gearbox fault diagnosis under limited labeled samples. First, the proposed SSMPN utilizes the powerful few-shot learning ability of the prototype network to learn the feature mapping and obtains prototypes by using limited labeled samples. Then, a threshold selection based on Monte Carlo uncertainty is adopted in pseudo label learning to increase the confidence of pseudo labels. Finally, the expended labeled dataset is utilized to optimize feature extraction and the momentum prototype method is proposed to fine-tune the prototype of each category. The experiments on both test-bench and wind turbine gearbox fault diagnosis demonstrated that SSMPN is more effective than the comparable methods under the same situation.

Design of the Wind Turbine Gearbox Fault Diagnosis System Based on LabVIEW

In the research history of the wind turbine gearbox fault diagnosis, artificial neural network plays an important role. However, due to the shortcomings of single neural network, the accuracy of fault diagnosis is not high. The fault diagnosis system of wind turbine gearbox based on LabVIEW was designed to monitor the speed, the power, the oil temperature of the bearing, the pressure, the vibration signal etc. Mathematical morphology was used to preprocess the collected data, such as filtering, and then four different neural networks used for primary diagnosis. The diagnosis results of each network were smoothed by mathematical morphology operation for the final diagnosis result. The experimental results show that the system has an excellent human-computer interaction interface to analyze and diagnose its operational status. The application of mathematical morphology combined with the multi-neural network in the fault diagnosis of wind turbine gearbox is conducive to reducing the uncertainty. It can correctly diagnose the fault type and improve the accuracy in the case of fault characteristics which are similar.

Adaptive multiple second-order synchrosqueezing wavelet transform and its application in wind turbine gearbox fault diagnosis

Wind turbines usually operate in harsh environments and in working conditions of variable speed, which easily causes their key components such as gearboxes to fail. The gearbox vibration signal of a wind turbine has nonstationary characteristics, and the existing time-frequency (TF) analysis (TFA) methods have some problems such as insufficient concentration of TF energy. In order to obtain a more apparent and more congregated time-frequency representation (TFR), this paper proposes a new TFA method, namely adaptive multiple second-order synchrosqueezing wavelet transform (AMWSST2). Firstly, a short-time window is innovatively introduced on the foundation of classical continuous wavelet transform, and the window width is adaptively optimized by using the center frequency and scale factor. After that, a smoothing process is carried out between different segments to eliminate the discontinuity and thus adaptive wavelet transform is generated. Then, on the basis of the theoretical framework of synchrosqueezing transform and accurate instantaneous frequency estimation by the utilization of second-order local demodulation operator, adaptive second-order synchrosqueezing wavelet transform (AWSST2) is formed. Considering that the quality of actual TFA is greatly disturbed by noise components, through performing multiple synchrosqueezing operations, the congregation of TFR energy is further improved, and finally, the AMWSST2 algorithm studied in this paper is proposed. Since synchrosqueezing operations are performed only in the frequency direction, this method AMWSST2 allows the signal to be perfectly reconstructed. For the verification of its effectiveness, this paper applies it to the processing of the vibration signal of the gearbox of a 750 kW wind turbine.

Wind turbine gearbox fault diagnosis based on an improved supervised autoencoder using vibration and motor current signals

It is an important research topic to learn features automatically from raw signals of wind turbines. Although vibration signals have been widely used for fault diagnosis, the inevitable disturbances and measurement noise make it difficult for vibration signals to completely reflect the fault conditions. In order to capture more sensitive features and generate accurate fault diagnosis results, a multi-source data fusion method based on an autoencoder (AE) is proposed in this paper, where the vibration and motor current signals are adopted. Compared with standard AE, the proposed method applies label information to the training process of AE, which is achieved by adding a new supervised penalty term to the hidden layer of AE. In this way, the learned features are supposed to be close to the class features within, and separate from the inter-class features; thus, the separability of the learned features is enhanced. The proposed method is used to merge the vibration signal and motor current signal of the wind turbine gearbox, and the results show that the fused data generated by the proposed method achieve a higher and more robust performance.

A novel wind turbine gearbox fault diagnosis method based on ASO-VMD and NRF

The combination of feature extraction and pattern recognition can make it possible to realize wind turbine gearboxes based on vibration signals. However, these methods need to be constantly adjusted parameters and spend time training when processing different vibration signals, which is time-consuming. Aiming at reducing the number of parameters that need to be adjusted and training time, this paper proposes a variational mode decomposition (VMD) based on atomic search optimization (ASO) and neural random forest (NRF) fault diagnosis model. The parameters of the VMD are adaptively adjusted by the ASO, which has the advantages of less adjustment parameters. After ASO-VMD decomposition, signals will be used as the input of NRF. We evaluate our method on simulation gearbox model which is established by Solidworks and Adams. Experimental results show that our method has faster training speed and higher recognition accuracy without set many parameters manually.

Fault Diagnosis of Wind Turbine Gearbox Based on Neighborhood QPSO and Improved D-S Evidence Theory

Background: Gearbox is the key equipment of wind turbine drive chain. Due to the harsh operating environment of wind turbine, gearbox failures occur frequently. Methods: To improve the accuracy of fault identification for wind turbine gearbox, an intelligent fault diagnosis method based on Neighborhood Quantum Particle Swarm Optimization (NQSPO) and improved Dempster-Shafer (D-S) evidence theory is proposed. In NQPSO algorithm, the best solution information in the neighborhood is introduced to guide the individual search behavior and enhance the population diversity. Also, the consistency coefficient is used to determine the weight of evidence, and the original evidence is amended to enhance the ability of D-S theory to fuse conflict evidence. Results: Experimental results show that the proposed method can overcome the influence of bad evidence on the diagnosis result and has high reliability. Conclusion: The research can effectively improve the accuracy of fault diagnosis of wind turbine gearbox, and provide a feasible idea for the fault diagnosis of nonlinear complex system.

Fault Diagnosis of Wind Turbine Gearbox Based on Improved QPSO Algorithm

Background: The effective diagnosis of wind turbine gearbox fault is an important means to ensure the normal and stable operation and avoid unexpected accidents. Methods: To accurately identify the fault modes of the wind turbine gearbox, an intelligent diagnosis technology based on BP neural network trained by the Improved Quantum Particle Swarm Optimization Algorithm (IQPSOBP) is proposed. In IQPSO approach, the random adjustment scheme of contractionexpansion coefficient and the restarting strategy are employed, and the performance evaluation is executed on a set of benchmark test functions. Subsequently, the fault diagnosis model of the wind turbine gearbox is built by using IQPSO algorithm and BP neural network. Results: According to the evaluation results, IQPSO is superior to PSO and QPSO algorithms. Also, compared with BP network, BP network trained by Particle Swarm Optimization (PSOBP) and BP network trained by Quantum Particle Swarm Optimization (QPSOBP), IQPSOBP has the highest diagnostic accuracy. Conclusion: The presented method provides a new reference for the fault diagnosis of wind turbine gearbox.

Dual-Enhanced Sparse Decomposition for Wind Turbine Gearbox Fault Diagnosis

The gearbox is one of the most important components in a wind turbine (WT) system, and fault diagnosis of WT gearbox for maintenance cost reduction is of paramount importance. However, fault feature identification is a primary challenge in gearbox fault diagnosis because weak fault features are always obscured by heavy background noise and multiple harmonic interferences. In this paper, a dual-enhanced sparse decomposition (DESD) method is proposed to address the feature enhancement and identification for gearbox fault vibration signal. Within the proposed method, the nonconvex generalized minimax-concave (GMC) penalty is used to construct the sparse-regularized cost function, the convexity of which can be maintained and the cost function can be minimized using convex optimization algorithms to obtain its global minimum. Furthermore, an adaptive regularization parameter selection scheme is proposed for the proposed DESD method in signal decomposition and feature extraction. Simulation studies and a real case study validate that the proposed method can better preserve the feature components of interest and can significantly improve the estimation accuracy. The comparison studies also show that the proposed method outperforms those methods with L1 norm regularization and spectral kurtosis.

Wind turbine gearbox fault diagnosis based on the vibration spectrum analysis

Gear box has the high failure rate among the components in wind turbines and the vibration signal analysis is the most common technology in its fault diagnosis. Gearbox vibration signals of wind turbines have complicated compositions, strong background noise and obvious non-stationary characteristi cs. The new method research suited to non-stationary and non-gaussian characteristics of vibration signals is the key to improve the level of fault diagnosis technology. In order to diagnosis the operation status of wind turbines, based on the spectral analysis and the combination of vibration wave spectrum analysis of wind turbine gearbox fault diagnosis method is proposed. Through the gearbox oil spectral analysis, tests the main metal content in the oil, the preliminary judgment out of the wind turbine operation working condition. Using the vibration waveform frequency spectrum analysis method can get the fault severity, and analyzes the cause of the problem. This method can realize the wind turbine gearbox fault diagnosis, to ensure reliability. It is can effectively conduct the wind turbine development, design and optimization, and reduces maintenance costs.

Wind turbine gearbox condition monitoring and fault diagnosis based on multi-sensor information fusion of SCADA and DSER-PSO-WRVM method

ABSTRACTA gearbox is one of the important parts in wind turbines (WTs). This part has a high downtime rate. In this paper, a structure is presented based on SCADA information fusion, and there is no need for adding new sensors and extra data collection equipment. The proposed method is an intelligent method based on generation and extraction of a feature from residual signal. The behavioural model of gearbox thermal signals is extracted from an Artificial Neural Network (ANN). The feature vector is the input of the feature level fusion algorithm. Relevance Vector Machine (RVM) is used for the feature level fusion. RVM is a kernel-based learning algorithm. Wavelet (W) basis is used as kernel function. To determine the optimal parameters of WRVM, Particle Swarm Optimization (PSO) is used as optimization algorithm. The initial decision-making in each PSO-WRVM block made the decision matrix based on the intensity of the fault. This matrix is the input of the Dempster–Shafer evidential reasoning (DSER) for the final decision-making. The simulation and experimental are conducted based on the data from a real 2.5 MW WT with faulty and healthy gearboxes. Results show that the proposed strategy is successful in diagnosis of gearbox serious damages.

Multiscale Filtering Reconstruction for Wind Turbine Gearbox Fault Diagnosis Under Varying-Speed and Noisy Conditions

One challenge in the gearbox fault diagnosis of variable-speed wind turbines via the popular vibration signal analysis is that, the signal spectrum is smeared by the time-varying shaft rotating speed and contaminated by some speed-unrelated components and noise. This paper proposes a new method, called multiscale filtering reconstruction, for wind turbine gearbox fault diagnosis under varying-speed and noisy conditions. The major contribution is that the new method can solve the spectrum smearing problem of the speed-related components while suppressing the speed-unrelated components and noise simultaneously. First, the time-variant vibration envelope signal is decomposed into time-variant monocomponent signals via a Vold–Kalman filter-based multiscale filtering algorithm. Then, the monocomponent signals are converted to be time-invariant by using a signal reform formula. Finally, a purified signal is reconstructed by synthesizing the reformed signals with different weighting factors. A gearbox fault diagnostic scheme based on the proposed method is developed for wind turbines equipped with doubly fed induction generators, in which the rotating frequency of the selected shaft used for multiscale filtering is estimated from the generator rotor current signals. Experimental studies on different gearbox faults are conducted to validate the proposed method and its superiority over the traditional angular resampling method.

Intelligent Fault Diagnosis of Rotary Machinery Based on Unsupervised Multiscale Representation Learning

The performance of traditional vibration based fault diagnosis methods greatly depends on those handcrafted features extracted using signal processing algorithms, which require significant amounts of domain knowledge and human labor, and do not generalize well to new diagnosis domains. Recently, unsupervised representation learning provides an alternative promising solution to feature extraction in traditional fault diagnosis due to its superior learning ability from unlabeled data. Given that vibration signals usually contain multiple temporal structures, this paper proposes a multiscale representation learning (MSRL) framework to learn useful features directly from raw vibration signals, with the aim to capture rich and complementary fault pattern information at different scales. In our proposed approach, a coarse-grained procedure is first employed to obtain multiple scale signals from an original vibration signal. Then, sparse filtering, a newly developed unsupervised learning algorithm, is applied to automatically learn useful features from each scale signal, respectively, and then the learned features at each scale to be concatenated one by one to obtain multiscale representations. Finally, the multiscale representations are fed into a supervised classifier to achieve diagnosis results. Our proposed approach is evaluated using two different case studies: motor bearing and wind turbine gearbox fault diagnosis. Experimental results show that the proposed MSRL approach can take full advantages of the availability of unlabeled data to learn discriminative features and achieved better performance with higher accuracy and stability compared to the traditional approaches.

Energy Dissipation and Vibration Control: Modeling, Algorithm, and Devices

The topic of vibration control and energy dissipation is among the oldest and most relevant in the field of engineering [...]

Wind Turbine Gearbox Fault Diagnosis Based on Improved EEMD and Hilbert Square Demodulation

The rapid expansion of wind farms has accelerated research into improving the reliability of wind turbines to reduce operational and maintenance costs. A critical component in wind turbine drive-trains is the gearbox, which is prone to different types of failures due to long-term operation under tough environments, variable speeds and alternating loads. To detect gearbox fault early, a method is proposed for an effective fault diagnosis by using improved ensemble empirical mode decomposition (EEMD) and Hilbert square demodulation (HSD). The method was verified numerically by implementing the scheme on the vibration signals measured from bearing and gear test rigs. In the implementation process, the following steps were identified as being important: (1) in order to increase the accuracy of EEMD, a criterion of selecting the proper resampling frequency for raw vibration signals was developed; (2) to select the fault related intrinsic mode function (IMF) that had the biggest kurtosis index value, the resampled signal was decomposed into a series of IMFs; (3) the selected IMF was demodulated by means of HSD, and fault feature information could finally be obtained. The experimental results demonstrate the merit of the proposed method in gearbox fault diagnosis.

Gearbox Fault Diagnosis in a Wind Turbine Using Single Sensor Based Blind Source Separation

This paper presents a single sensor based blind source separation approach, namely, the wavelet-assisted stationary subspace analysis (WSSA), for gearbox fault diagnosis in a wind turbine. Continuous wavelet transform (CWT) is used as a preprocessing tool to decompose a single sensor measurement data into a set of wavelet coefficients to meet the multidimensional requirement of the stationary subspace analysis (SSA). The SSA is a blind source separation technique that can separate the multidimensional signals into stationary and nonstationary source components without the need for independency and prior information of the source signals. After that, the separated nonstationary source component with the maximum kurtosis value is analyzed by the enveloping spectral analysis to identify potential fault-related characteristic frequencies. Case studies performed on a wind turbine gearbox test system verify the effectiveness of the WSSA approach and indicate that it outperforms independent component analysis (ICA) and empirical mode decomposition (EMD), as well as the spectral-kurtosis-based enveloping, for wind turbine gearbox fault diagnosis.

Wind turbine gearbox fault diagnosis based on symmetrical components and frequency domain

The region of Adrar in Algeria is the windiest in the country; it is for this reason the electricity and gas company “Sonelgaz” has placed a wind farm of about 10 MW. Since it is a Saharan region, the wind is sandy, rich in silica that can damage the teeth of wind gearbox. The present work proposes a method which can make an early diagnosis of the broken tooth and its location in the gearbox, before the general breakdown. The modeling of defaults inside the turbine gearbox by symmetrical components technique is proposed successfully and for the first time in the field of diagnostics (usually this technique is used in the power grid lines short circuits analysis), associated with frequency response analysis. In addition, as it is impossible to install sensors at different parts, the increase in temperature and vibration inside the gearbox due to default, a sensor-less fault diagnosis based on the analysis of power signal generated by the wind turbine is presented and detailed. Furthermore, the peculiarity of this work is the modeling of unbalance forces caused by teeth failure on both the slow shaft and the fast shaft of the gearbox, which has never been considered before. It has been proven that this proposal allows a precise location of the broken tooth and the concerned gear inside the gearbox.