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

scholarly journals Health Assessment and Remaining Useful Life Prediction of Wind Turbine High-Speed Shaft Bearings

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4612
Author(s):  
Zhenen Li ◽  
Xinyan Zhang ◽  
Tusongjiang Kari ◽  
Wei Hu
Keyword(s):  
Wind Turbine ◽  
Life Prediction ◽  
High Speed ◽  
Comprehensive Evaluation ◽  
Health Assessment ◽  
Remaining Useful Life ◽  
Degradation Model ◽  
Bayesian Update ◽  
Som Network ◽  
Useful Life

Vibration signals contain abundant information that reflects the health status of wind turbine high-speed shaft bearings ((HSSBs). Accurate health assessment and remaining useful life (RUL) prediction are the keys to the scientific maintenance of wind turbines. In this paper, a method based on the combination of a comprehensive evaluation function and a self-organizing feature map (SOM) network is proposed to construct a health indicator (HI) curve to characterizes the health state of HSSBs. Considering the difficulty in obtaining life cycle data of similar equipment in a short time, the exponential degradation model is selected as the degradation trajectory of HSSBs on the basis of the constructed HI curve, the Bayesian update model, and the expectation–maximization (EM) algorithm are used to predict the RUL of HSSBs. First, the time domain, frequency domain, and time–frequency domain degradation features of HSSBs are extracted. Second, a comprehensive evaluation function is constructed and used to select the degradation features with good performance. Third, the SOM network is used to fuse the selected degradation features to construct a one-dimensional HI curve. Finally, the exponential degradation model is selected as the degradation trajectory of HSSBs, and the Bayesian update and EM algorithm are used to predict the RUL of the HSSB. The monitoring data of a wind turbine HSSB in actual operation is used to validate the model. The HI curve constructed by the method in this paper can better reflect the degradation process of HSSBs. In terms of life prediction, the method in this paper has better prediction accuracy than the SVR model.

Hybrid method for remaining useful life prediction in wind turbine systems

2018 ◽  
Vol 116 ◽  
pp. 173-187 ◽  
Author(s):  
M.A. Djeziri ◽  
S. Benmoussa ◽  
R. Sanchez
Keyword(s):  
Wind Turbine ◽  
Hybrid Method ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Useful Life

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 Remaining Useful Life Estimation of Wind Turbine Blades under Variable Wind Speed Conditions Using Particle Filters

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Bhavana Valeti ◽  
Shamim N. Pakzad
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Turbine Blades ◽  
Remaining Useful Life ◽  
Structural Components ◽  
Wind Turbine Blades ◽  
Average Wind Speed ◽  
Average Wind ◽  
Useful Life ◽  
Variable Wind

Rotor blades are the most complex structural components in a wind turbine and are subjected to continuous cyclic loads of wind and self-weight variation. The structural maintenance operations in wind farms are moving towards condition based maintenance (CBM) to avoid premature failures. For this, damage prognosis with remaining useful life (RUL) estimation in wind turbine blades is necessary. Wind speed variation plays an important role influencing the loading and consequently the RUL of the structural components. This study investigates the effect of variable wind speed between the cutin and cut-out speeds of a typical wind farm on the RUL of a damage detected wind turbine blade as opposed to average wind speed assumption. RUL of wind turbine blades are estimated for different initial crack sizes using particle filtering method which forecasts the evolution of fatigue crack addressing the non-linearity and uncertainty in crack propagation. The stresses on a numerically simulated life size onshore wind turbine blade subjected to the above wind speed loading cases are used in computing the crack propagation observation data for particle filters. The effects of variable wind speed on the damage propagation rates and RUL in comparison to those at an average wind speed condition are studied and discussed.

A hybrid DBN-SOM-PF-based prognostic approach of remaining useful life for wind turbine gearbox

2020 ◽  
Vol 152 ◽  
pp. 138-154 ◽  
Author(s):  
Yubin Pan ◽  
Rongjing Hong ◽  
Jie Chen ◽  
Weiwei Wu
Keyword(s):  
Wind Turbine ◽  
Remaining Useful Life ◽  
Wind Turbine Gearbox ◽  
Useful Life

scholarly journals Prognosis of the Remaining Useful Life of Bearings in a Wind Turbine Gearbox

Energies ◽  
2016 ◽  
Vol 10 (1) ◽  
pp. 32 ◽  
Author(s):  
Wei Teng ◽  
Xiaolong Zhang ◽  
Yibing Liu ◽  
Andrew Kusiak ◽  
Zhiyong Ma
Keyword(s):  
Wind Turbine ◽  
Remaining Useful Life ◽  
Wind Turbine Gearbox ◽  
Useful Life

scholarly journals PCA based health indicator for remaining useful life prediction of wind turbine gearbox

2019 ◽  
Vol 29 ◽  
pp. 31-36
Author(s):  
Sabareesh G R ◽  
Hemanth Mithun Praveen ◽  
Divya Shah ◽  
Krishna Dutt Pandey ◽  
Vamsi I
Keyword(s):  
Wind Turbine ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Health Indicator ◽  
Wind Turbine Gearbox ◽  
Useful Life

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.

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