The Application of Dual-Tree Complex Wavelet Transform (DTCWT) Energy Entropy in Misalignment Fault Diagnosis of Doubly-Fed Wind Turbine (DFWT)

Xiao, Yancai; Hong, Yi; Xiu-hai, Chen; Chen, Weijia

doi:10.3390/e19110587

Cited by 29 publications

(30 citation statements)

References 13 publications

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“…The acceleration signal of the high-speed output of the gearbox is used as the vibration signal (details in literature [40]). In MATLAB, the wind turbine and its control system are established to obtain the stator current for misalignment faults (details in literature [41]). The simulation model of the wind turbine is in the Maximum Power Points Tracking (MPPT) stage, in which the input speed is 81.3 • /s, and the vibration signal and stator current signal are obtained under parallel misalignment fault for researching.…”

Section: Signal Acquisitionmentioning

confidence: 99%

Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model

Xiao

Hua

2020

Algorithms

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Due to the harsh working environment of wind turbines, various types of faults are prone to occur during long-term operation. Misalignment faults between the gearbox and the generator are one of the latent common faults for doubly-fed wind turbines. Compared with other faults like gears and bearings, the prediction research of misalignment faults for wind turbines is relatively few. How to accurately predict its developing trend has always been a difficulty. In this paper, a combined forecasting model is proposed for misalignment fault prediction of wind turbines based on vibration and current signals. In the modelling, the improved Multivariate Grey Model (IMGM) is used to predict the deterministic trend and the Least Squares Support Vector Machine (LSSVM) optimized by quantum genetic algorithm (QGA) is adopted to predict the stochastic trend of the fault index separately, and another LSSVM optimized by QGA is used as a non-linear combiner. Multiple information of time-domain, frequency-domain and time-frequency domain of the wind turbine's vibration or current signals are extracted as the input vectors of the combined forecasting model and the kurtosis index is regarded as the output. The simulation results show that the proposed combined model has higher prediction accuracy than the single forecasting models.Algorithms 2020, 13, 56 For the complex non-linear system, a single forecasting model is not enough to obtain ideal prediction results. Therefore, in order to predict the mechanical fault accurately, the combined forecasting model has attracted more and more attention from scholars. For example, in Ref.[18], the improved Grey Model (GM (1,1)) and the Back Propagation (BP) neural network optimized by Genetic Algorithm (GA) were used as the single forecasting models. The minimum sum of error squares was used as the combination principle to assign appropriate weight coefficients to them. The combined forecasting model had a smaller prediction error. Ref. [19] proposed a calculation method of combined weight coefficients for the unequal weight of error. The combined forecasting model was constructed by Multivariate Grey Model (MGM (1, n)) and Extreme Learning Machine (ELM) neural network. The combined forecasting model was more suitable for predicting the trend of the bearing fault. In Ref.[20], according to the minimum variance principle, Support Vector Machine (SVM) and grey model were combined to make up the shortcomings of single forecasting models. In Ref. [21], SVM was used as the combiner of forecasting models. The Kalman filter, BP neural network and SVM model were used as single forecasting models. The prediction errors of the single forecasting models were larger than that of the combined model. In Ref. [22], the BP neural network was used to determine the weight coefficients of each single forecasting model. The combined forecasting model using GM (1,1,θ) optimized by Particle Swarm Optimization (PSO) algorithm and SVM optimized by PSO achieved better prediction accuracy for the short-term load of...

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Section: Signal Acquisitionmentioning

confidence: 99%

Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model

Xiao

Hua

2020

Algorithms

Self Cite

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“…The Shannon wavelet support vector machine is applied to dealing with the low-dimensional eigenvectors which are compressed from high-dimensional feature. In addition, Xiao et al [147] propose a method based on dual-tree complex wavelet transform energy entropy to classify the misalignment of the transmission system of the wind turbine. In order to solve the problem of insufficient sensor data from the working engine, information entropy and deep belief networks are used for gas turbine engine fault diagnosis in [148].…”

Section: Typical Entropy Theories Application On Fault Diagnosis Of Omentioning

confidence: 99%

“…In [159], singular spectrum entropy, power spectrum entropy, and approximate entropy are extracted in vibration signals by Shannon entropy, and the feature fusion model is constructed to classify and diagnose the fault signals. Chen et al [145] variational mode decomposition + energy entropy 3 Tang et al [146] manifold learning + Shannon wavelet support vector machine 4 Xiao et al [147] dual-tree complex wavelet transform + energy entropy 5 Feng et al [148] information entropy + deep belief networks 6 Yin et al [149] time-frequency entropy enhancement + boundary constraint assisted relative gray relational grade 7 Chen et al [150] ensemble multiwavelet + Shannon entropy 8…”

Section: Typical Entropy Theories Application On Fault Diagnosis Of Omentioning

confidence: 99%

Related Entropy Theories Application in Condition Monitoring of Rotating Machineries

Liu

Zhi

Zhang

et al. 2019

Entropy

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Rotating machinery plays an important role in various kinds of industrial engineering. How to assess their conditions is a key problem for operating safety and condition-based maintenance. The potential anomaly, fault and failure information can be obtained by analyzing the collected condition monitoring data of the previously deployed sensors in rotating machinery. Among the available methods of analyzing sensors data, entropy and its variants can provide quantitative information contained in these sensing data. For implementing fault detection, diagnosis, and prognostics, this information can be utilized for feature extraction and selecting appropriate training data for machine learning methods. This article aims to review the related entropy theories which have been applied for condition monitoring of rotating machinery. This review consists of typical entropy theories presentation, application, summary, and discussion.

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“…It is worth noting that diagnosis of system units at a system level considers a fault in the system as a failure of a system unit. We do not consider the details of what was wrong with the failed unit as it was done, for example, in the fault diagnosis described in [9][10][11]. In view of this, we do not classify the faults as it was done, for example, in [12,13].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Intermittently Faulty Units at System Level

Mashkov

Fišer

Lytvynenko

et al. 2019

Data

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Mostly, diagnosis at a system level intends to identify only permanently faulty units. In the paper, we consider the case when both permanently and intermittently faulty units can occur in the system. Identification of intermittently faulty units has some specifics which we have considered in this paper. We also suggest the method which allows for distinguishing among different types of intermittent faults. A diagnosis procedure was suggested for each type of intermittent fault.

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The Application of Dual-Tree Complex Wavelet Transform (DTCWT) Energy Entropy in Misalignment Fault Diagnosis of Doubly-Fed Wind Turbine (DFWT)

Cited by 29 publications

References 13 publications

Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model

Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model

Related Entropy Theories Application in Condition Monitoring of Rotating Machineries

Diagnosis of Intermittently Faulty Units at System Level

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