Reliability analysis and data driven modelling of railway component failure

Reliability analysis and modelling of the railway component failure modes and mechanisms are essential for its proper functional performance, effective maintenance, and safe operation. However, it is challenging to apply the theoretical methods to the in-field failure record data which are heterogen...

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Main Author: Wang, Jinlong
Other Authors: Pang Hock Lye, John
Format: Thesis-Doctor of Philosophy
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/159237
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Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-159237
record_format dspace
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Mechanical engineering
spellingShingle Engineering::Mechanical engineering
Wang, Jinlong
Reliability analysis and data driven modelling of railway component failure
description Reliability analysis and modelling of the railway component failure modes and mechanisms are essential for its proper functional performance, effective maintenance, and safe operation. However, it is challenging to apply the theoretical methods to the in-field failure record data which are heterogeneous in format. According to the data type, this research established a systematic historical maintenance data analysis framework in capturing the dynamic behavior of reliability indicators related conditioning variables. Failure modes and mechanisms from five components were investigated which including: (1) Welded rail break failure; (2) Rotating bending fatigue test for welded rail material; (3) Train door failure; (4) Rail wear degradation failure; (5) Wheel flat degradation failure. The one-dimensional time-to-failure type data sets in the first three cases were modeled by Weibull distribution models to characterize the reliability. Such statistical models are under some distribution assumptions which may not be fully satisfied in practice, but the models can be fitted with limited data samples and their interpretabilities are high. Data sets in cases (4) and (5) have larger data size and higher feature dimension which contain richer information. Therefore, data-driven machine learning based methods have been developed and modified to provide better degradation modelling capabilities. The novelty of this research mainly including: designing customized raw data processing, developing robust modelling framework, and providing implementable knowledge feedback for maintenance application. A novel method of combining Support Vector Regression with Archard wear law to predict the wear behavior of the rail steel was developed in case study (4). The proposed physical knowledge guided robust nonlinear regression analysis framework for multidimensional degradation data dealing with atypical hidden outliers demonstrated significant improvement in the modelling results. Pre-process of the raw wear data which involving feature importance analysis, physical model guided feature generation and outlier detection is developed within the framework for the SVR model’s robust learning. A Support Vector Classification model was developed in case study (5) to predict the alarm level of defect wheels three to five days before the Wheel Impact Load Detector (WILD) monitoring system actual report which could gain a longer time window for the maintenance resource arrangement. A novel wheel flat size prediction method based on Gaussian Process Regression model and Principle Component Analysis is another major contribution in this work. Human interpretable linear functions were derived for the in-field judgement of defect wheel flat size. Additionally, the established principles for predicting defect wheel flat sizes contribute to the optimization of alarm thresholds to improve maintenance efficiency. This research adopted a range of process techniques and algorithms to develop effective frameworks for railway component failure maintenance data clean. A WILD sensor big data set containing of 40 million samples was transformed through a proposed varied time window feature extraction method to integrate with another maintenance measurement dataset precisely. The developed feature extraction method guaranteed a larger amount of available training data to improve the machine learning accuracy. This research also paid attention to the interpretation and visualization of machine leaning based reliability analysis models and results. To explain the machine learning algorithm underlying mechanism, a game theory based SHAP(SHapley Additive exPlanations) analysis and a Principle Components Analysis based feature reduction methods were developed for machine learning explain and visualize accordingly.
author2 Pang Hock Lye, John
author_facet Pang Hock Lye, John
Wang, Jinlong
format Thesis-Doctor of Philosophy
author Wang, Jinlong
author_sort Wang, Jinlong
title Reliability analysis and data driven modelling of railway component failure
title_short Reliability analysis and data driven modelling of railway component failure
title_full Reliability analysis and data driven modelling of railway component failure
title_fullStr Reliability analysis and data driven modelling of railway component failure
title_full_unstemmed Reliability analysis and data driven modelling of railway component failure
title_sort reliability analysis and data driven modelling of railway component failure
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/159237
_version_ 1761781967264677888
spelling sg-ntu-dr.10356-1592372023-03-11T18:10:28Z Reliability analysis and data driven modelling of railway component failure Wang, Jinlong Pang Hock Lye, John School of Mechanical and Aerospace Engineering MHLPANG@ntu.edu.sg Engineering::Mechanical engineering Reliability analysis and modelling of the railway component failure modes and mechanisms are essential for its proper functional performance, effective maintenance, and safe operation. However, it is challenging to apply the theoretical methods to the in-field failure record data which are heterogeneous in format. According to the data type, this research established a systematic historical maintenance data analysis framework in capturing the dynamic behavior of reliability indicators related conditioning variables. Failure modes and mechanisms from five components were investigated which including: (1) Welded rail break failure; (2) Rotating bending fatigue test for welded rail material; (3) Train door failure; (4) Rail wear degradation failure; (5) Wheel flat degradation failure. The one-dimensional time-to-failure type data sets in the first three cases were modeled by Weibull distribution models to characterize the reliability. Such statistical models are under some distribution assumptions which may not be fully satisfied in practice, but the models can be fitted with limited data samples and their interpretabilities are high. Data sets in cases (4) and (5) have larger data size and higher feature dimension which contain richer information. Therefore, data-driven machine learning based methods have been developed and modified to provide better degradation modelling capabilities. The novelty of this research mainly including: designing customized raw data processing, developing robust modelling framework, and providing implementable knowledge feedback for maintenance application. A novel method of combining Support Vector Regression with Archard wear law to predict the wear behavior of the rail steel was developed in case study (4). The proposed physical knowledge guided robust nonlinear regression analysis framework for multidimensional degradation data dealing with atypical hidden outliers demonstrated significant improvement in the modelling results. Pre-process of the raw wear data which involving feature importance analysis, physical model guided feature generation and outlier detection is developed within the framework for the SVR model’s robust learning. A Support Vector Classification model was developed in case study (5) to predict the alarm level of defect wheels three to five days before the Wheel Impact Load Detector (WILD) monitoring system actual report which could gain a longer time window for the maintenance resource arrangement. A novel wheel flat size prediction method based on Gaussian Process Regression model and Principle Component Analysis is another major contribution in this work. Human interpretable linear functions were derived for the in-field judgement of defect wheel flat size. Additionally, the established principles for predicting defect wheel flat sizes contribute to the optimization of alarm thresholds to improve maintenance efficiency. This research adopted a range of process techniques and algorithms to develop effective frameworks for railway component failure maintenance data clean. A WILD sensor big data set containing of 40 million samples was transformed through a proposed varied time window feature extraction method to integrate with another maintenance measurement dataset precisely. The developed feature extraction method guaranteed a larger amount of available training data to improve the machine learning accuracy. This research also paid attention to the interpretation and visualization of machine leaning based reliability analysis models and results. To explain the machine learning algorithm underlying mechanism, a game theory based SHAP(SHapley Additive exPlanations) analysis and a Principle Components Analysis based feature reduction methods were developed for machine learning explain and visualize accordingly. Doctor of Philosophy 2022-06-02T05:11:11Z 2022-06-02T05:11:11Z 2021 Thesis-Doctor of Philosophy Wang, J. (2021). Reliability analysis and data driven modelling of railway component failure. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/159237 https://hdl.handle.net/10356/159237 en This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). application/pdf Nanyang Technological University