Artificial intelligence based condition monitoring of rail infrastructure

Wasim Ahmad

Research output: ThesisPd Eng ThesisAcademic

28 Downloads (Pure)

Abstract

The design cycle of the rail condition monitoring system (CMS) consists of problem investigation, treatment design, and validation. The aim of the project is to improve the rail maintenance by timely reporting the incipient rail defects. Ontime and appropriatemaintenance results in reduction ofmaintenance cost, short down-time and high service availability. A massive data has been collected from railway system through various sensors but the connection between the sensors data and the rail condition is not known. Moreover, currently the maintenance strategies are triggered mostly based on human inspection. Therefore an automated rail CMS need to be developed that makes intelligent decisions using the data and helps in initiating a timely maintenance process. The rail defects need to be detected at their earliest stageswhich could otherwise lead to severe defects and cause rail failure. Therefore the aimed system will help in carrying out predictive maintenance of the rail infrastructure. The detailed description of the problem
is discussed in chapter 1. The solution for the design problem is build upon the need and requirements of the stakeholders and system. Everything that the stakeholders
expect from this solution is included in the list of requirements. Moreover, requirements at the system level are also identified and aimed to be achieved. A comprehensive list of requirements is given table 2.1 in chapter 2.
The design of the rail CMS is based on the train axle box acceleration (ABA) data, that is used by the machine learning (ML) pipeline for information retrieval about rail condition. The designed ML pipeline for rail CMS is illustrated in figure 3.1 of chapter 3. The pipeline consists of ABA pre-processing, extraction of features by using time domain analysis, and anomaly detection algorithm for detecting irregular patterns in ABA. The algorithm for anomaly detection is presented in detail in chapter 4. The validation process is based on the comparison of the actual rail defects and anomalies detected by the algorithm in ABA data. The flowchart for the validation process is shown in figure 5.1 in chapter 5. Video images of rail infrastructure are utilized for performing the validation process. The visible rail defects in the images are manually labelled and feed to the validation model for comparison. The performance metrics such as hits, mishits and false alarms etc. are calculated using the validation model. The design and user guide for the graphical
user interface (GUI) of rail CMS is covered in chapter 6 that explains various components in the layout and discusses the inputs and outputs of the system. Finally the discussion, conclusions, and recommendations are presented in chapter 7 of the thesis report.
Original languageEnglish
Awarding Institution
  • University of Twente
Supervisors/Advisors
  • Tinga, Tiedo , Supervisor
  • Loendersloot, Richard , Co-Supervisor
Award date24 Sep 2019
Place of PublicationEnschede
Publisher
Publication statusPublished - 24 Sep 2019

Fingerprint

Condition monitoring
Artificial intelligence
Rails
Axles
Defects
Pipelines
Learning systems
Time domain analysis
Sensors
Cost reduction
Information retrieval

Cite this

Ahmad, W. (2019). Artificial intelligence based condition monitoring of rail infrastructure. Enschede: University of Twente.
Ahmad, Wasim . / Artificial intelligence based condition monitoring of rail infrastructure. Enschede : University of Twente, 2019. 81 p.
@phdthesis{32f41e07dca04be6a61f353a31c15902,
title = "Artificial intelligence based condition monitoring of rail infrastructure",
abstract = "The design cycle of the rail condition monitoring system (CMS) consists of problem investigation, treatment design, and validation. The aim of the project is to improve the rail maintenance by timely reporting the incipient rail defects. Ontime and appropriatemaintenance results in reduction ofmaintenance cost, short down-time and high service availability. A massive data has been collected from railway system through various sensors but the connection between the sensors data and the rail condition is not known. Moreover, currently the maintenance strategies are triggered mostly based on human inspection. Therefore an automated rail CMS need to be developed that makes intelligent decisions using the data and helps in initiating a timely maintenance process. The rail defects need to be detected at their earliest stageswhich could otherwise lead to severe defects and cause rail failure. Therefore the aimed system will help in carrying out predictive maintenance of the rail infrastructure. The detailed description of the problemis discussed in chapter 1. The solution for the design problem is build upon the need and requirements of the stakeholders and system. Everything that the stakeholdersexpect from this solution is included in the list of requirements. Moreover, requirements at the system level are also identified and aimed to be achieved. A comprehensive list of requirements is given table 2.1 in chapter 2.The design of the rail CMS is based on the train axle box acceleration (ABA) data, that is used by the machine learning (ML) pipeline for information retrieval about rail condition. The designed ML pipeline for rail CMS is illustrated in figure 3.1 of chapter 3. The pipeline consists of ABA pre-processing, extraction of features by using time domain analysis, and anomaly detection algorithm for detecting irregular patterns in ABA. The algorithm for anomaly detection is presented in detail in chapter 4. The validation process is based on the comparison of the actual rail defects and anomalies detected by the algorithm in ABA data. The flowchart for the validation process is shown in figure 5.1 in chapter 5. Video images of rail infrastructure are utilized for performing the validation process. The visible rail defects in the images are manually labelled and feed to the validation model for comparison. The performance metrics such as hits, mishits and false alarms etc. are calculated using the validation model. The design and user guide for the graphicaluser interface (GUI) of rail CMS is covered in chapter 6 that explains various components in the layout and discusses the inputs and outputs of the system. Finally the discussion, conclusions, and recommendations are presented in chapter 7 of the thesis report.",
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Ahmad, W 2019, 'Artificial intelligence based condition monitoring of rail infrastructure', University of Twente, Enschede.

Artificial intelligence based condition monitoring of rail infrastructure. / Ahmad, Wasim .

Enschede : University of Twente, 2019. 81 p.

Research output: ThesisPd Eng ThesisAcademic

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T1 - Artificial intelligence based condition monitoring of rail infrastructure

AU - Ahmad, Wasim

PY - 2019/9/24

Y1 - 2019/9/24

N2 - The design cycle of the rail condition monitoring system (CMS) consists of problem investigation, treatment design, and validation. The aim of the project is to improve the rail maintenance by timely reporting the incipient rail defects. Ontime and appropriatemaintenance results in reduction ofmaintenance cost, short down-time and high service availability. A massive data has been collected from railway system through various sensors but the connection between the sensors data and the rail condition is not known. Moreover, currently the maintenance strategies are triggered mostly based on human inspection. Therefore an automated rail CMS need to be developed that makes intelligent decisions using the data and helps in initiating a timely maintenance process. The rail defects need to be detected at their earliest stageswhich could otherwise lead to severe defects and cause rail failure. Therefore the aimed system will help in carrying out predictive maintenance of the rail infrastructure. The detailed description of the problemis discussed in chapter 1. The solution for the design problem is build upon the need and requirements of the stakeholders and system. Everything that the stakeholdersexpect from this solution is included in the list of requirements. Moreover, requirements at the system level are also identified and aimed to be achieved. A comprehensive list of requirements is given table 2.1 in chapter 2.The design of the rail CMS is based on the train axle box acceleration (ABA) data, that is used by the machine learning (ML) pipeline for information retrieval about rail condition. The designed ML pipeline for rail CMS is illustrated in figure 3.1 of chapter 3. The pipeline consists of ABA pre-processing, extraction of features by using time domain analysis, and anomaly detection algorithm for detecting irregular patterns in ABA. The algorithm for anomaly detection is presented in detail in chapter 4. The validation process is based on the comparison of the actual rail defects and anomalies detected by the algorithm in ABA data. The flowchart for the validation process is shown in figure 5.1 in chapter 5. Video images of rail infrastructure are utilized for performing the validation process. The visible rail defects in the images are manually labelled and feed to the validation model for comparison. The performance metrics such as hits, mishits and false alarms etc. are calculated using the validation model. The design and user guide for the graphicaluser interface (GUI) of rail CMS is covered in chapter 6 that explains various components in the layout and discusses the inputs and outputs of the system. Finally the discussion, conclusions, and recommendations are presented in chapter 7 of the thesis report.

AB - The design cycle of the rail condition monitoring system (CMS) consists of problem investigation, treatment design, and validation. The aim of the project is to improve the rail maintenance by timely reporting the incipient rail defects. Ontime and appropriatemaintenance results in reduction ofmaintenance cost, short down-time and high service availability. A massive data has been collected from railway system through various sensors but the connection between the sensors data and the rail condition is not known. Moreover, currently the maintenance strategies are triggered mostly based on human inspection. Therefore an automated rail CMS need to be developed that makes intelligent decisions using the data and helps in initiating a timely maintenance process. The rail defects need to be detected at their earliest stageswhich could otherwise lead to severe defects and cause rail failure. Therefore the aimed system will help in carrying out predictive maintenance of the rail infrastructure. The detailed description of the problemis discussed in chapter 1. The solution for the design problem is build upon the need and requirements of the stakeholders and system. Everything that the stakeholdersexpect from this solution is included in the list of requirements. Moreover, requirements at the system level are also identified and aimed to be achieved. A comprehensive list of requirements is given table 2.1 in chapter 2.The design of the rail CMS is based on the train axle box acceleration (ABA) data, that is used by the machine learning (ML) pipeline for information retrieval about rail condition. The designed ML pipeline for rail CMS is illustrated in figure 3.1 of chapter 3. The pipeline consists of ABA pre-processing, extraction of features by using time domain analysis, and anomaly detection algorithm for detecting irregular patterns in ABA. The algorithm for anomaly detection is presented in detail in chapter 4. The validation process is based on the comparison of the actual rail defects and anomalies detected by the algorithm in ABA data. The flowchart for the validation process is shown in figure 5.1 in chapter 5. Video images of rail infrastructure are utilized for performing the validation process. The visible rail defects in the images are manually labelled and feed to the validation model for comparison. The performance metrics such as hits, mishits and false alarms etc. are calculated using the validation model. The design and user guide for the graphicaluser interface (GUI) of rail CMS is covered in chapter 6 that explains various components in the layout and discusses the inputs and outputs of the system. Finally the discussion, conclusions, and recommendations are presented in chapter 7 of the thesis report.

M3 - Pd Eng Thesis

PB - University of Twente

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Ahmad W. Artificial intelligence based condition monitoring of rail infrastructure. Enschede: University of Twente, 2019. 81 p.