Wind Turbine Gearbox Fault Research Articles

Investigating parallel multi-step vibration processing pipelines for planetary stage fault detection in wind turbine drivetrains

This paper proposes a signal processing approach for wind turbine gearbox vibration signals based on employing multiple analysis pipelines. These so-called pipelines consist of combinations of various advanced signal processing methods that have been proven to be effective in literature when applied to wind turbine vibration signals. The performance of the pipelines is examined on vibration data containing different wind turbine gearbox faults in the planetary stages. Condition indicators are extracted from every pipeline to evaluate the fault detection capability for such incipient failures. The results indicate that the multipronged approach with the different pipelines increases the reliability of successfully detecting incipient planetary stage gearbox faults. The type, location, and severity of the fault influences the choice for the appropriate processing method combination. It is therefore often insufficient to only utilize a single processing pipeline for vibration analysis of wind turbine gearbox faults. Besides investigating the performance of the different processing techniques, the main outcome and recommendation of this paper is thus to employ a diversified analysis methodology which is not limited to a sole method combination, to improve the early detection rate of planetary stage gearbox faults.

Utilisation of Ensemble Empirical Mode Decomposition in Conjunction with Cyclostationary Technique for Wind Turbine Gearbox Fault Detection

In this paper the application of cyclostationary signal processing in conjunction with Ensemble Empirical Mode Decomposition (EEMD) technique, on the fault diagnostics of wind turbine gearboxes is investigated and has been highlighted. It is shown that the EEMD technique together with cyclostationary analysis can be used to detect the damage in complex and non-linear systems such as wind turbine gearbox, where the vibration signals are modulated with carrier frequencies and are superimposed. In these situations when multiple faults alongside noisy environment are present together, the faults are not easily detectable by conventional signal processing techniques such as FFT and RMS.

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.

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.

Compound faults diagnosis and analysis for a wind turbine gearbox via a novel vibration model and empirical wavelet transform

Fault diagnosis of a wind turbine gearbox can schedule maintenance strategy on a wind turbine and save operational cost for wind farms. Owing to low rotational speed and weak vibration energy, fault diagnosis for planetary stage is arduous in a wind turbine gearbox involving multi-stage transmissions. A novel modulation model is presented to support the fault diagnosis of the planetary stage of a wind turbine gearbox, which is described as the mesh frequency of intermediate stage, high speed stage, or mechanical natural frequency of the gearbox is a carrier wave modulated by the mesh frequency of planetary stage with distributed faults. Even possessing this presented vibration model, the fault feature of planetary stage with lower rotational speed is easily concealed by the meshing vibration energy of ordinary stages with higher speed, especially when faults simultaneously arise in the ordinary stages. Aiming at the diagnosis of the above compound faults in an industrial wind turbine gearbox, empirical wavelet transform is utilized to adaptively find weak fault frequency in planetary stage as well as evident fault characteristics in other ordinary stages.

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.

Stochastic simulation assessment of an automated vibration-based condition monitoring framework for wind turbine gearbox faults

Effectively monitoring the health of a wind turbine gearbox is a complex and often multidisciplinary endeavor. Recently, condition monitoring practices increasingly combine knowledge from fields like signal processing, machine learning, and mechanics. Such a diverse approach becomes necessary when dealing with the vast amount of data that is generated by the multitude of sensors that are typically placed on a wind turbine gearbox. Ideally, this approach needs to be automated and scalable as well, since it is unfeasible to perform all the necessary processing work manually in a continuous manner. This paper focuses on assessing the performance of such an automated processing framework for the case of gearbox fault detection using vibration measurements. A year of vibration measurements on a gearbox is simulated by stochastic variation of the operating conditions and the system behavior. A bearing fault is progressively introduced as to track the detection capabilities of the framework in such stochastic circumstances. The used signal model is based on previously obtained experience with experimental data sets originating from wind turbine gearboxes. The framework itself consists of multiple pre-processing steps where each step tries to deal with compensating for the external or unwanted influences such as speed variation or noise. Finally, multiple features are calculated on the pre-processed signals and trended as to see whether the processing scheme can provide any benefit compared to basic traditional statistical indicators. It is shown that the multi-step pre-processing approach is beneficial and robust for the advanced feature calculation and thus the early fault detection.

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.

Data-Driven Diagnosis of Nonlinearly Mixed Mechanical Faults in Wind Turbine Gearbox

This letter proposes an efficient algorithmic solution to diagnose multiple mechanical faults in wind turbine gearbox through source number estimation using an empirical mode decomposition (EMD) and singular value decomposition (SVD) joint approach, and source signal recovery based on short-time Fourier transform (STFT), fuzzy $\boldsymbol {C}$ -means clustering and $\boldsymbol {l_{1}}$ norm decomposition. The effectiveness of the solution is validated using real wind turbine measurements under multifault scenarios.

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 [...]

Tacholess order-tracking approach for wind turbine gearbox fault detection

Monitoring of wind turbines under variable-speed operating conditions has become an important issue in recent years. The gearbox of a wind turbine is the most important transmission unit; it generally exhibits complex vibration signatures due to random variations in operating conditions. Spectral analysis is one of the main approaches in vibration signal processing. However, spectral analysis is based on a stationary assumption and thus inapplicable to the fault diagnosis of wind turbines under variable-speed operating conditions. This constraint limits the application of spectral analysis to wind turbine diagnosis in industrial applications. Although order-tracking methods have been proposed for wind turbine fault detection in recent years, current methods are only applicable to cases in which the instantaneous shaft phase is available. For wind turbines with limited structural spaces, collecting phase signals with tachometers or encoders is difficult. In this study, a tacholess order-tracking method for wind turbines is proposed to overcome the limitations of traditional techniques. The proposed method extracts the instantaneous phase from the vibration signal, resamples the signal at equiangular increments, and calculates the order spectrum for wind turbine fault identification. The effectiveness of the proposed method is experimentally validated with the vibration signals of wind turbines.

Study of transfer path of wind turbine gearbox fault vibration signal based on power flow finite element method

As core components of wind power equipment, wind turbine gearboxes usually work in hostile conditions, which accounts for the increasing failure rate year by year. Therefore, the condition monitori...

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.

A new wind turbine fault diagnosis method based on ensemble intrinsic time-scale decomposition and WPT-fractal dimension

In this paper an ensemble intrinsic time-scale decomposition (EITD) method based on the cubic spline interpolation and linear transformation of intrinsic time-scale decomposition (ITD) was proposed, which can restrain the end effect and avoid the signal distortion. Combining ensemble intrinsic time-scale decomposition (EITD) with wavelet packet transform (WPT) and correlation dimension (CD), a novel method for decomposing nonstationary vibration signal and diagnosing wind turbine faults is presented. In implementation of the method, wavelet packet transform is employed to denoise raw vibration signals. Some important influencing factors relating directly to the computational precision of correlation dimension are discussed. The advantage of combining EITD and fractal dimension is that it does recognize the wind turbine gearbox fault types, and can solve the difficulty of recognizing fault conditions when two or more fractal dimensions are close to each other. To verify the effectiveness of the EITD-WPT-CD in detecting the faults, their induced vibrations are collected from high speed shaft gear under normal and faulty conditions through acceleration measurement. The results show that this method is capable of extracting the signal features and identifying the working conditions. The fault diagnosis application in a wind turbine gearbox indicates that the proposed method improved the accuracy of fault diagnosis.

Wind Turbine Gearbox Fault Diagnosis Based on Improved HHT

The randomness of wind power and the nonstationarity of gearbox vibration signals greatly increased the difficulty of wind turbine gearbox faults feature diagnosis. Studies proved that Hilbert-Huang transform (HHT) has a satisfying performance when it be used to analyze nonlinear and non-stationary signals, but two troublesome problems in application of HHT are end effect problem and false intrinsic mode functions (IMFs). An improved solution method based on adaptive waveform matching endpoint extension is proposed to solve end effect problem. Meanwhile, an improved energy conservation method is proposed to eliminate false IMFs. Tests prove that improved HHT can greatly improve the accuracy of wind turbine gearbox faults diagnosis and with a balance efficiency.

Quantitative Analysis of Fault of Wind Turbines Based on Hilbert Space Feature Entropy

Aim at the problem of wind turbine gearbox fault diagnosis , a method of Hilbert space feature entropy is proposed. Using the information entropy theory, the faults information obtained in the HHT spectrum quantify to the value of Hilbert space feature entropy, thus achieving quantitative analysis of the failure of the wind turbine gearbox. The method was applied to fault diagnosis of the wind turbine gearboxes, which proved that the method is efficacious.

Side‐band algorithm for automatic wind turbine gearbox fault detection and diagnosis

Improving the availability of wind turbines is critical for minimising the cost of wind energy, especially offshore. The development of reliable and cost-effective gearbox condition monitoring systems (CMSs) is of concern to the wind industry, because the gearbox downtime has a significant effect on the wind turbine availabilities. Timely detection and diagnosis of developing gear defects is essential for minimising an unplanned downtime. One of the main limitations of most current CMSs is the time consuming and costly manual handling of large amounts of monitoring data, therefore automated algorithms would be welcome. This study presents a fault detection algorithm for incorporation into a commercial CMS for automatic gear fault detection and diagnosis. Based on the experimental evidence from the Durham Condition Monitoring Test Rig, a gear condition indicator was proposed to evaluate the gear damage during non-stationary load and speed operating conditions. The performance of the proposed technique was then successfully tested on signals from a full-size wind turbine gearbox that had sustained gear damage, and had been studied in a National Renewable Energy Laboratory's (NREL) programme. The results show that the proposed technique proves efficient and reliable for detecting gear damage. Once implemented into the wind turbine CMSs, this algorithm can automate the data interpretation, thus reducing the quantity of the information that the wind turbine operators must handle.