scholarly journals Remaining Useful Life Prediction on Wind Turbine Gearbox

2021 ◽  
Vol 9 (5) ◽  
pp. 57-65
Author(s):  
R Srinivasan ◽  
T Paul Robert
Keyword(s):  
Data Analysis ◽  
Wind Turbine ◽  
Weak Link ◽  
Damage Model ◽  
Remaining Useful Life ◽  
Performance Data ◽  
Data Driven ◽  
Wind Turbine Gearbox ◽  
Life Data ◽  
Useful Life

This research proposes a methodology to estimate the reliability of gearbox using life data analysis and predict the Lifetime Use Estimation (LUE). Life data analysis involves collection of historical field replacements of gearbox and perform statistical analysis such as Weibull analysis to estimate the reliability. Remaining useful life is estimated by using Cumulative damage model and data-driven methods. The first approach is based on the physics of failure models of degradation and the second approach is based on the operational, environmental & loads data provided by the design team which is translated into a mathematical model that represent the behavior of the degradation. Data-driven method is used in this research, where the different performance data from components are exploited to model the degradation's behavior. LUE is used to make key business decisions such as planning of spares, service cost and increase availability of wind turbine. Gearbox is the heart of the wind turbine and it is made up of several stages of helical/planetary gears. Performance data is acquired separately for each of these stages and LUE is calculated individually. The individual LUE is then rolled up to estimate the overall Lifetime Use Estimation of gearbox. This will identify the weak link which is going to fail first and the failure mode which is driving the primary failure can be identified. Finally, corrective measures can be planned accordingly. The cumulated damage and LUE are estimated by using Inverse power law damage model along with Miner’s rule.

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 Wind turbine gearbox failure and remaining useful life prediction using machine learning techniques

Wind Energy ◽  
2018 ◽  
Vol 22 (3) ◽  
pp. 360-375 ◽  
Author(s):  
James Carroll ◽  
Sofia Koukoura ◽  
Alasdair McDonald ◽  
Anastasis Charalambous ◽  
Stephan Weiss ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wind Turbine ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Machine Learning Techniques ◽  
Wind Turbine Gearbox ◽  
Learning Techniques ◽  
Useful Life

scholarly journals Wind Turbine Main Bearing Fatigue Life Estimation with Physics-informed Neural Networks

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Yigit Anil Yucesan ◽  
Felipe A. C. Viana
Keyword(s):  
Neural Networks ◽  
Fatigue Life ◽  
Wind Turbine ◽  
Failure Modes ◽  
Damage Model ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Neural Network Modeling ◽  
Main Bearing ◽  
Daunting Task

Unexpected main bearing failure on a wind turbine causes unwanted maintenance and increased operation costs (mainly due to crane, parts, labor, and production loss). Unfortunately, historical data indicates that failure can happen far earlier than the design lives of the component. Root cause analysis investigations have pointed to problems inherent from manufacturing as the major contributor, as well as issues related to event loads (e.g., startups, shutdowns, and emergency stops), extreme environmental conditions, and maintenance practices, among others. Altogether, the multiple failure modes and contributors make modeling remaining useful life of main bearings a very daunting task. In this paper, we present a novel physics-informed neural network modeling approach for main bearing fatigue. The proposed approach is fully hybrid and designed to merge physics- informed and data-driven layers within deep neural networks. The result is a cumulative damage model where the physics-informed layers are used model the relatively well-understood physics (L10 fatigue life) and the data-driven layers account for the hard to model components (e.g., contribution due to poor greasing conditions).

scholarly journals Failure and Remaining Useful Life Prediction of Wind Turbine Gearboxes

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Sofia Koukoura
Keyword(s):  
Wind Turbine ◽  
Life Prediction ◽  
Expert Knowledge ◽  
Remaining Useful Life ◽  
Wind Turbine Gearbox ◽  
Life Estimation ◽  
Knowledge Based ◽  
Incipient Faults ◽  
Frame Work ◽  
Useful Life

The purpose of this project is to predict wind turbine gearbox incipient faults using a combination of condition monitoring data. It is expected to contribute in developing a robust frame-work for wind turbine gearbox component incipient failure prediction and remaining useful life estimation. It further pro-poses a solution on how to overcome the challenges of expert knowledge based systems using AI techniques. Wind turbine operation and maintenance decision making confidence can be therefore increased.

scholarly journals Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2135
Author(s):  
Marcin Witczak ◽  
Marcin Mrugalski ◽  
Bogdan Lipiec
Keyword(s):  
Historical Data ◽  
Field Effect Transistors ◽  
Failure Process ◽  
Practical Method ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Data Repository ◽  
Uncertainty Interval ◽  
Useful Life ◽  
Takagi Sugeno

The paper presents a new method of predicting the remaining useful life of technical devices. The proposed soft computing approach bridges the gap between analytical and data-driven health prognostic approaches. Whilst the former ones are based on the classical exponential shape of degradation, the latter ones learn the degradation behavior from the observed historical data. As a result of the proposed fusion, a practical method for calculating components’ remaining useful life is proposed. Contrarily to the approaches presented in the literature, the proposed ensemble of analytical and data-driven approaches forms the uncertainty interval containing an expected remaining useful life. In particular, a Takagi–Sugeno multiple models-based framework is used as a data-driven approach while an exponential curve fitting on-line approach serves as an analytical one. Unlike conventional data-driven methods, the proposed approach is designed on the basis of the historical data that apart from learning is also applied to support the diagnostic decisions. Finally, the entire scheme is used to predict power Metal Oxide Field Effect Transistors’ (MOSFETs) health status. The status of the currently operating MOSFET is determined taking into consideration the knowledge obtained from the preceding MOSFETs, which went through the run-to-failure process. Finally, the proposed approach is validated with the application of real data obtained from the NASA Ames Prognostics Data Repository.

A Clustering-Based Framework for Performance Degradation Prediction of Slewing Bearing Using Multiple Physical Signals

2019 ◽  
Vol 5 (2) ◽  
Author(s):  
Peng Ding ◽  
Hua Wang ◽  
Yongfen Dai
Keyword(s):  
Degradation Process ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Self Organizing Map ◽  
Rolling Bearings ◽  
Heavy Load ◽  
Joint Application ◽  
Learning Modes ◽  
Useful Life ◽  
Slewing Bearings

Diagnosing the failure or predicting the performance state of low-speed and heavy-load slewing bearings is a practical and effective method to reduce unexpected stoppage or optimize the maintenances. Many literatures focus on the performance prediction of small rolling bearings, while studies on slewing bearings' health evaluation are very rare. Among these rare studies, supervised or unsupervised data-driven models are often used alone, few researchers devote to remaining useful life (RUL) prediction using the joint application of two learning modes which could fully take diversity and complexity of slewing bearings' degradation and damage into consideration. Therefore, this paper proposes a clustering-based framework with aids of supervised models and multiple physical signals. Correlation analysis and principle component analysis (PCA)-based multiple sensitive features in time-domain are used to establish the performance recession indicators (PRIs) of torque, temperature, and vibration. Subsequently, these three indicators are divided into several parts representing different degradation periods via optimized self-organizing map (OSOM). Finally, corresponding data-driven life models of these degradation periods are generated. Experimental results indicate that multiple physical signals can effectively describe the degradation process. The proposed clustering-based framework is provided with a more accurate prediction of slewing bearings' RUL and well reflects the performance recession periods.

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