scholarly journals Adaptive Machine Learning Approach for Fault Prognostics based on Normal Conditions - Application to Shaft Bearings of Wind Turbine

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Koceila Abid ◽  
Moamar Sayed-Mouchaweh ◽  
Cornez Laurence
Keyword(s):  
Wind Turbine ◽  
High Speed ◽  
Remaining Useful Life ◽  
Normal Operation ◽  
Maintenance Cost ◽  
Support Vector ◽  
Industrial Systems ◽  
Operation Conditions ◽  
Degradation Data ◽  
Fault Prognostics

Prognostics can enhance the reliability and availability of industrial systems while reducing unscheduled faults and maintenance cost. In real industrial systems, data collected from the normal operation conditions of system is available, but there is a lack of historical degradation data is often unavailable. Hence, this paper proposes a general data-driven prognostic approach dealing with the lack of degradation data in the offline phase. First, features are computed on the collected raw signal, then One Class Support Vector Machine (OCSVM) is used to detect the degradation, this anomaly detection method is trained using only normal operation data. Then, features are ranked according to the selection criteria. The feature having the highest score is chosen as Health Indicator (HI). Finally an adaptive degradation model is applied for the prediction of the degradation evolution over time and Remaining Useful Life (RUL) estimation. The proposed approach is validated using run-to-failure vibration data collected from a high speed shaft bearings of a commercial wind turbine.

Fault prognostics of rolling element bearing based on feature extraction and supervised machine learning: Application to shaft wind turbine gearbox using vibration signal

2020 ◽  
pp. 095440622097615
Author(s):  
Fawzi Gougam ◽  
Rahmoune Chemseddine ◽  
Djamel Benazzouz ◽  
Khaled Benaggoune ◽  
Noureddine Zerhouni
Keyword(s):  
Machine Learning ◽  
Wind Turbine ◽  
Energy Demand ◽  
High Speed ◽  
Remaining Useful Life ◽  
Rolling Element Bearing ◽  
Supervised Machine Learning ◽  
Electricity Production ◽  
Inference System ◽  
Fault Prognostics

Renewable energies offer new solutions to an ever-increasing energy demand. Wind energy is one of the main sources of electricity production, which uses winds to be converted to electrical energy with lower cost and environment saving. The major failures of a wind turbine occur in the bearings of high-speed shafts. This paper proposes the use of optimized machine learning to predict the Remaining Useful Life (RUL) of bearing based on vibration data and features extraction. Significant features are extracted from filtered band-pass of the squared raw signal where the health indicators are automatically selected using relief technique. Optimized Adaptive Neuro Fuzzy Inference System (ANFIS) by Partical Swarm Optimization (PSO) is used to model the non linear degradation of the extracted indicators. The proposed approach is applied on experimental setup of wind turbine where the results show its effectiveness for RUL estimation.

scholarly journals A Hybrid Remaining Useful Life Prediction Method for Cutting Tool Considering the Wear State

2022 ◽  
Author(s):  
Yifan Li ◽  
Yongyong Xiang ◽  
Baisong Pan ◽  
Luojie Shi
Keyword(s):  
Tool Wear ◽  
High Speed ◽  
Cutting Tool ◽  
Bayesian Framework ◽  
Remaining Useful Life ◽  
Physical Models ◽  
Data Driven ◽  
Support Vector ◽  
Wear Model ◽  
Useful Life

Abstract Accurate cutting tool remaining useful life (RUL) prediction is of significance to guarantee the cutting quality and minimize the production cost. Recently, physics-based and data-driven methods have been widely used in the tool RUL prediction. The physics-based approaches may not accurately describe the time-varying wear process due to a lack of knowledge for underlying physics and simplifications involved in physical models, while the data-driven methods may be easily affected by the quantity and quality of data. To overcome the drawbacks of these two approaches, a hybrid prognostics framework considering tool wear state is developed to achieve an accurate prediction. Firstly, the mapping relationship between the sensor signal and tool wear is established by support vector regression (SVR). Then, the tool wear statuses are recognized by support vector machine (SVM) and the results are put into a Bayesian framework as prior information. Thirdly, based on the constructed Bayesian framework, parameters of the tool wear model are updated iteratively by the sliding time window and particle filter algorithm. Finally, the tool wear state space and RUL can be predicted accordingly using the updating tool wear model. The validity of the proposed method is demonstrated by a high-speed machine tool experiment. The results show that the presented approach can effectively reduce the uncertainty of tool wear state estimation and improve the accuracy of RUL prediction.

scholarly journals Vestas V90-3MW Wind Turbine Gearbox Health Assessment Using a Vibration-Based Condition Monitoring System

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
A. Romero ◽  
Y. Lage ◽  
S. Soua ◽  
B. Wang ◽  
T.-H. Gan
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Learning Algorithm ◽  
Health Assessment ◽  
Field Trials ◽  
Health Condition ◽  
Normal Operation ◽  
Wind Turbine Gearbox ◽  
Operation Conditions

Reliable monitoring for the early fault diagnosis of gearbox faults is of great concern for the wind industry. This paper presents a novel approach for health condition monitoring (CM) and fault diagnosis in wind turbine gearboxes using vibration analysis. This methodology is based on a machine learning algorithm that generates a baseline for the identification of deviations from the normal operation conditions of the turbine and the intrinsic characteristic-scale decomposition (ICD) method for fault type recognition. Outliers picked up during the baseline stage are decomposed by the ICD method to obtain the product components which reveal the fault information. The new methodology proposed for gear and bearing defect identification was validated by laboratory and field trials, comparing well with the methods reviewed in the literature.

An integrated methodology for estimating the remaining useful life of high-speed wind turbine shaft bearings with limited samples

2021 ◽  
Author(s):  
Boualem Merainani ◽  
Sofiane Laddada ◽  
Eric Bechhoefer ◽  
Mohamed Abdessamed Ait Chikh ◽  
Djamel Benazzouz
Keyword(s):  
Wind Turbine ◽  
High Speed ◽  
Remaining Useful Life ◽  
Integrated Methodology ◽  
Turbine Shaft ◽  
Useful Life

Vibrations of a Three-Bladed Wind Turbine Rotor Due to Classical Flutter

2002 ◽  
Author(s):  
M. H. Hansen
Keyword(s):  
Wind Turbine ◽  
Critical Frequency ◽  
Turbine Blades ◽  
Turbine Rotor ◽  
Normal Operation ◽  
Aerodynamic Damping ◽  
Operation Conditions ◽  
Torsional Frequency ◽  
Classical Flutter ◽  
Wind Turbine Rotor

The aeroelastic stability of a three-bladed wind turbine is considered with respect to classical flutter. Previous studies have shown that the risk of stall-induced vibrations of turbine blades is related to the dynamics of the complete turbine, for example does the aerodynamic damping of a rotor whirling mode depend highly on the tower stiffness. The results of this paper indicate that the turbine dynamics also affect the risk of flutter. The study is based on an eigenvalue analysis of a linear aeroelastic turbine model. In an example of a MW sized turbine, the critical frequency of the first torsional blade mode is determined for which flutter can occur under normal operation conditions. It is shown that this critical torsional frequency is higher when the blades are interacting through the hub with the remaining turbine, than when all blades are rigidly clamped at the root. Thus, the dynamics of the turbine has increased the risk of flutter.

Assessing the Importance of Geometric Nonlinear Effects in the Prediction of Wind Turbine Blade Loads

2015 ◽  
Vol 10 (4) ◽  
Author(s):  
D. I. Manolas ◽  
V. A. Riziotis ◽  
S. G. Voutsinas
Keyword(s):  
Wind Turbine ◽  
Nonlinear Effects ◽  
Normal Operation ◽  
Order Model ◽  
Multibody Model ◽  
Operation Conditions ◽  
Geometric Nonlinear ◽  
Structural Nonlinearities ◽  
Clear Shift ◽  
Blade Loads

As the size of commercial wind turbines increases, new blade designs become more flexible in order to comply with the requirement for reduced weights. In normal operation conditions, flexible blades undergo large bending deflections, which exceed 10% of their radius, while significant torsion angles toward the tip of the blade are obtained, which potentially affect performance and stability. In the present paper, the effects on the loads of a wind turbine from structural nonlinearities induced by large deflections of the blades are assessed, based on simulations carried out for the NREL 5 MW wind turbine. Two nonlinear beam models, a second order (2nd order) model and a multibody model that both account for geometric nonlinear structural effects, are compared to a first order beam (1st order) model. Deflections and loads produced by finite element method based aero-elastic simulations using these three models show that the bending–torsion coupling is the main nonlinear effect that drives differences on loads. The main effect on fatigue loads is the over 100% increase of the torsion moment, having obvious implications on the design of the pitch bearings. In addition, nonlinearity leads to a clear shift in the frequencies of the second edgewise modes.

Research on Fault Diagnosis Method of Wind Turbine Based on Wavelet Analysis and LS-SVM

2013 ◽  
Vol 724-725 ◽  
pp. 593-597 ◽  
Author(s):  
Chang Liang Liu ◽  
Wei Xue Qi
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Wind Turbine ◽  
High Speed ◽  
Wavelet Decomposition ◽  
Least Square ◽  
Support Vector ◽  
Vibration Signals ◽  
Training Samples ◽  
Diagnosis Method

Aiming at the fault characteristics of high-speed gearbox fault diagnosis of wind turbine, a fault diagnosis method of combining wavelet analysis with least square-support vector machine (LS-SVM) is proposed. According to the method, the energy of frequency bands generated by wavelet decomposition and reconstruction of the high-speed gearbox's vibration signals in different fault states is normalized as eigenvectors, forming training and testing samples of LS-SVM fault classifier. Train the LS-SVM fault diagnosis model with the training samples and test the accuracy with the testing samples. The result of research shows that the fault diagnosis method based on the wavelet analysis and LS-SVM has good diagnostics effect.

Dynamic Analysis of the Wind Turbine Tower

2013 ◽  
Vol 790 ◽  
pp. 651-654
Author(s):  
Chi Chen ◽  
Hong Bo Shen ◽  
Min Wang
Keyword(s):  
Dynamic Analysis ◽  
Wind Turbine ◽  
Natural Frequencies ◽  
Safety Evaluation ◽  
Model Analysis ◽  
Rotational Frequency ◽  
Normal Operation ◽  
Transient Dynamic Analysis ◽  
Operation Conditions ◽  
Wind Turbine Tower

In this thesis, the conical tower of domestic popular 1.5MW wind turbine is analyzed in dynamic by using the software ANSYS. The natural frequencies can be extracted from the model analysis results, comparing them with the impeller rotational frequency and determining whether the tower will resonate when the wind turbine under normal operation conditions. Based on the model analysis, the transient dynamic analysis is carried out by inputting the history records of seismic wave acceleration, Both these two analysis can provide the basis for the safety evaluation of the tower.

scholarly journals Prediction of Remaining Useful Life of Wind Turbine Bearings under Non-Stationary Operating Conditions

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3318 ◽  
Author(s):  
Lixiao Cao ◽  
Zheng Qian ◽  
Hamid Zareipour ◽  
David Wood ◽  
Ehsan Mollasalehi ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Wavelet Packet ◽  
Health Indicators ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Support Vector ◽  
Useful Life ◽  
Artificial Neural Network Ann

Wind-powered electricity generation has grown significantly over the past decade. While there are many components that might impact their useful life, the gearbox and generator bearings are among the most fragile components in wind turbines. Therefore, the prediction of remaining useful life (RUL) of faulty or damaged wind turbine bearings will provide useful support for reliability evaluation and advanced maintenance of wind turbines. This paper proposes a data-driven method combining the interval whitenization method with a Gaussian process (GP) algorithm in order to predict the RUL of wind turbine generator bearings. Firstly, a wavelet packet transform is used to eliminate noise in the vibration signals and extract the characteristic fault signals. A comprehensive analysis of the real degradation process is used to determine the indicators of degradation. The interval whitenization method is proposed to reduce the interference of non-stationary operating conditions to improve the quality of health indicators. Finally, the GP method is utilized to construct the model which reflects the relationship between the RUL and health indicators. The method is assessed using actual vibration datasets from two wind turbines. The prediction results demonstrate that the proposed method can reduce the effect of non-stationary operating conditions. In addition, compared with the support vector regression (SVR) method and artificial neural network (ANN), the prediction accuracy of the proposed method has an improvement of more than 65.8%. The prediction results verify the effectiveness and superiority of the proposed method.

Fault Diagnosis of Wind Turbine Gearbox Based on Least Square Support Vector Machine with Genetic Algorithm

2013 ◽  
Vol 846-847 ◽  
pp. 620-623
Author(s):  
Wen Qing Zhao ◽  
Rui Cai ◽  
Li Wei Wang ◽  
De Wen Wang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Least Square ◽  
Normal Operation ◽  
Support Vector ◽  
Wind Turbine Gearbox ◽  
Vector Method ◽  
Searching Ability

Gearbox affect the normal operation of the wind turbines, to study the fault diagnosis, support vector method was used. Parameters selection is very important and decides the fault diagnosis precision. In order to overcome the blindness of man-made choice of the parameters in least squares support vector machine (LSSVM) and improve the accuracy and efficiency of fault diagnosis, a method based on LSSVM trained by genetic algorithm was proposed. This method searches the optimized parameters in LSSVM by taking advantage of the genetic algorithms powerful global searching ability. The research is provided using this method on the fault diagnosis of wind turbine gearbox and compared with the diagnostic method of LSSVM. The experimental results show that the method achieves a higher diagnostic accuracy.

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