scholarly journals A Fatigue Life Prediction Method for the Drive System of Wind Turbine Using Internet of Things

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Hang Zhou ◽  
Shi-Jun Yi ◽  
Ya-Fei Liu ◽  
Yong-Quan Hu ◽  
Yong Xiang
Keyword(s):  
Fatigue Life ◽  
Internet Of Things ◽  
Wind Turbine ◽  
Life Prediction ◽  
Evaluation Method ◽  
Drive System ◽  
Time Varying ◽  
Analysis Model ◽  
Fatigue Life Analysis ◽  
Turbine Drive

The wind turbine drive system is one of the key components in converting wind energy into electrical energy. The life prediction of drive system is very important for the maintenance of wind turbine. With increasing capacity, the wind turbine system has become more complicated. Consequently, for the life prediction of drive system, it is necessary to consider the problems of multi-information fusion of big data, quantification of time-varying dynamic loads, and analysis of multiple-damage coupling. In order to solve the above challenges, the fatigue life analysis and evaluation method considering the interaction of coupled multiple damages are proposed in this study. The hierarchical Bayesian theory with fault physics technology is introduced to deal with the uncertainty of wind turbine drive system. Then, a time-varying performance analysis model is established based on the multiple-damage coupling competition failure mechanism. Moreover, the Internet of Things (IoT) technology is introduced and combined with the proposed model. Through the data collection by IoT, the time-stress curve of drive system can be obtained. A case study about the remaining fatigue life estimation of drive system is utilized to illustrate the effectiveness of the proposed method.

scholarly journals Reliability-Based Fatigue Life Prediction for Complex Structure with Time-Varying Surrogate Modeling

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Lu-Kai Song ◽  
Guang-Chen Bai ◽  
Cheng-Wei Fei ◽  
Jie Wen
Keyword(s):  
Fatigue Life ◽  
Computational Efficiency ◽  
Surrogate Model ◽  
Life Prediction ◽  
Complex Structure ◽  
Fatigue Life Prediction ◽  
General Regression Neural Network ◽  
Time Varying ◽  
Computational Accuracy ◽  
Turbine Blisk

To improve the computational efficiency and accuracy of reliability-based fatigue life prediction for complex structure, a time-varying particle swarm optimization- (PSO-) based general regression neural network (GRNN) surrogate model (called as TV/PSO-GRNN) is developed. By integrating the proposed space-filling Latin hypercube sampling technique and PSO-GRNN regression function, the mathematical model of TV/PSO-GRNN is studied. The reliability-based fatigue life prediction framework is illustrated in respect of the TV/PSO-GRNN surrogate model. Moreover, the reliability-based fatigue life prediction of an aircraft turbine blisk under multiphysics interaction is performed to validate the TV/PSO-GRNN model. We obtain the distributional characteristics, reliability degree, and sensitivity degree of fatigue failure cycle, which are useful for the turbine blisk design. By comparing the direct simulation (FE/FV model), RSM, GRNN, PSO-GRNN, and TV/PSO-GRNN, we observe that the TV/PSO-GRNN surrogate model is promising to perform the reliability-based fatigue life prediction of the turbine blisk and enhance the computational efficiency while ensuring an acceptable computational accuracy. The efforts of this study offer a useful insight for the reliability-based design optimization of complex structure.

Research on Commercial Vehicle Clutch Release Bearing Fatigue Life Prediction Model

2013 ◽  
Vol 423-426 ◽  
pp. 1853-1857
Author(s):  
Guo Liang Chen ◽  
Xiao Yang Chen
Keyword(s):  
Fatigue Crack ◽  
Fatigue Life ◽  
Prediction Model ◽  
Contact Stress ◽  
Life Prediction ◽  
High Speed ◽  
Commercial Vehicle ◽  
Smelting Process ◽  
Fatigue Life Analysis ◽  
Life Prediction Model

Commercial vehicle clutch release bearings working at high speed, strong vibration,high temperature, damp and easy pollution conditions. Fatigue life analysis is based on the release bearing rings or rolling body began to appear fatigue spalling, in which this kind of phenomenon is under cyclic stress. The contact stress distribution is not uniform, the contact stress is mainly concentrated near the surface; influenced by the geometry and physical properties and lubrication of the surface significantly. Contact between the two types of fatigue crack extension methods: fatigue crack surface under expansion and surface fatigue crack propagation. The surface crack growth mainly originated from two kinds of cases: crack caused by surface pre crack and contact between the two surface asperity each other. New life prediction model for the release bearing based on L-P theory and Tallian model ,in which influence factors of fatigue life is introduced on the smelting process, surface defect, surface roughness, residual stress, elastohydrodynamic lubrication oil film,environmental cleanliness, temperature, the effect of varying load characteristics and other factors of fatigue life. The results show that: the clutch release bearing life prediction model of new and more close to the real conditions of automobile clutch, provide the theory basis for the development of a new generation high-speed heavy-duty clutch release bearing of the commercial vehicle.

Fatigue Life Analysis of an Automotive Tensioner Through Strain-Life Approach

2015 ◽  
Author(s):  
Maryam Talimi ◽  
Jean W. Zu
Keyword(s):  
Fatigue Life ◽  
Stress Analysis ◽  
Drive System ◽  
Life Assessment ◽  
Fatigue Properties ◽  
Fatigue Life Assessment ◽  
Front End ◽  
Life Analysis ◽  
Fatigue Life Analysis ◽  
Prediction Approach

In this paper, fatigue life assessment of a tensioner is studied through dynamic load analysis, stress analysis, and stress-life fatigue analysis approach. Tensioner is a critical part of an automotive front end accessory drive system, providing pre-tension to the belt. The front end accessory drive systems are responsible for transmitting power from the crankshaft to the accessory components. Due to the engine pulsation, components of the accessory drive including the tensioner are subjected to dynamic loads leading to fatigue failure. The fatigue life assessment of a mechanical component highly depends on loading, geometry, and material properties. In addition, the dynamic behavior of the front end accessory drive is complicated due to coupling between several modes of vibrations in belt, pulleys, and the tensioner arm. Duo to the complexity of the parameters involved and complicated dynamics, the fatigue life analysis of FEAD components is a challenging task. This paper includes three main parts, namely stress analysis, fatigue properties prediction, and life estimation. The dynamic analysis of a generic front end accessory drive system is performed in order to obtain effective loads on the tensioner. Stress state for the tensioner in case of different applied loading conditions is performed via a series of Finite Element (FE) analyses, and the critical region of the part is determined. Finally, fatigue life is estimated through strain-life approach. Modest work has been found in this area providing a comprehensive solution to the fatigue life investigation of power train components. The present study offers a comprehensive modeling approach which predicts the automative tensioner lifetime. The lifetime of any FEAD system components can be determined using the developed fatigue life prediction approach.

Modification optimization-based fatigue life analysis and improvement of EMU gear

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yong-Hua Li ◽  
Chi Zhang ◽  
Hao Yin ◽  
Yang Cao ◽  
Xiaoning Bai
Keyword(s):  
Fatigue Life ◽  
Life Prediction ◽  
Engineering Practice ◽  
Analysis Method ◽  
Intelligent Algorithm ◽  
Transient Dynamic Analysis ◽  
Content Type ◽  
Gear Modification ◽  
Life Analysis ◽  
Fatigue Life Analysis

PurposeThis paper proposes an improved fatigue life analysis method for optimal design of electric multiple units (EMU) gear, which aims at defects of traditional Miner fatigue cumulative damage theory.Design/methodology/approachA fatigue life analysis method by modifying S–N curve and considering material difference is presented, which improves the fatigue life of EMU gear based on shape modification optimization. A corrected method for stress amplitude, average stress and S–N curve is proposed, which considers low stress cycle, material difference and other factors. The fatigue life prediction of EMU gear is carried out by corrected S–N curve and transient dynamic analysis. Moreover, the gear modification technology combined with intelligent optimization method is adopted to investigate the approach of fatigue life analysis and improvement.FindingsThe results show that it is more corresponded to engineering practice by using the improved fatigue life analysis method than the traditional method. The function of stress and modification amount established by response surface method meets the requirement of precision. The fatigue life of EMU gear based on the intelligent algorithm for seeking the optimal modification amount is significantly improved compared with that before the modification.Originality/valueThe traditional fatigue life analysis method does not consider the influence of working condition and material. The life prediction results by using the method proposed in this paper are more accurate and ensure the safety of the people in the EMU. At the same time, the combination of intelligent algorithm and gear modification can improve the fatigue life of gear on the basis of accurate prediction, which is of great significance to the portability of EMU maintenance.

Fatigue Life Prediction Methods Evaluation for Remanufacturing Mechanical Parts

2013 ◽  
Vol 579-580 ◽  
pp. 573-579 ◽  
Author(s):  
Jian Xin Weng ◽  
Wen Hui Yue ◽  
Yong Xing Zhu ◽  
Peng Hui Duan
Keyword(s):  
Finite Element ◽  
Fatigue Life ◽  
Prediction Accuracy ◽  
Life Prediction ◽  
Evaluation Method ◽  
Simulation Method ◽  
Working Environment ◽  
Fatigue Life Prediction ◽  
Mechanical Parts ◽  
Combined Simulation

Aiming at the demand of remanufacturing mechanical parts fatigue life prediction, the main methods of fatigue life prediction are reviewed and summarized. The finite element and dynamics combined simulation method has been widely used at present, whose advantages are that it is suitable for most of the mechanical parts, and the forecast cycle is short, and it can be analyzed combining with the parts actual working condition, but the prediction accuracy depends on the comprehensive degree to the service condition. The experimental method is the most traditional method, and the fatigue life value obtained by the method is reliable, but the method is entirely depend on experience, and the cost of experiments is expensive, so the feasibility is bad. The fatigue life analysis method can lower the dependence on large number of experiments, but there is a great distance between the predicting fatigue life and actual fatigue life in working environment. The metal magnetic memory non-destructive testing method doesnt damage the testing objects, but the method is still in the stage of further research at present. Finally, taking the number of experiments, prediction cycle, prediction accuracy, prediction cost and the complex degree of the principle involved in the prediction process as evaluation indexes, the finite element and dynamics combined simulation method is the best fatigue life prediction method according to the score values of each method calculated by the quantitative scores based on the expert evaluation method.

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