Use of parameter estimation for the detection and diagnosis of faults on electric train door systems

Dassanayake, H. P. B.; Roberts, Clive; Goodman, C.J.; Tobias, Andrew M.

doi:10.1243/1748006xjrr265

Cited by 14 publications

(8 citation statements)

References 9 publications

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“…In this area, there have been two approaches: model-based and data-driven. In the model-based approach, which utilizes a mathematical model describing the door dynamics, several studies have been attempted such as modeling the door mechanism by a simple ball screw table [3], modeling the motor dynamics by ordinary differential equations [4], and bond graph modeling to describe a train door mechatronic subsystems [5]. However, the train door system contains many components interconnected with various uncertainty, which makes the modeling approach of limited value in the diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Fault Diagnosis for Train Door System: Traditional versus Deep Learning Approaches

Ham

Han

Kim

et al. 2019

Sensors

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A fault diagnosis of a train door system is carried out using the motor current signal that operates the door. A test rig is prepared, in which various fault modes are examined by applying extreme conditions, as well as the natural and artificial wears of critical components. Two approaches are undertaken toward the fault classification for comparative purposes: one is the traditional feature-based method that requires several steps for the processing features such as signal segmentation, the extraction of time-domain features, selection by Fisher’s discrimination, and K-nearest neighbor. The other is the deep learning approach by employing the convolutional neural network (CNN) to skip the hand-crafted features extraction process. In the traditional approach, good accuracy is found only after the current signal is segmented into the three velocity regimes, which enhances the discrimination capability. In the CNN, superior accuracy is obtained even by the original raw signal, which is more convenient in terms of implementation. However, in view of practical applications, the traditional approach is more useful in that the features processing can be easily applied to assess the health state of each fault and monitor the progression over time in the real operation, which is not enabled by the deep learning approach.

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Section: Introductionmentioning

confidence: 99%

A Comparative Study of Fault Diagnosis for Train Door System: Traditional versus Deep Learning Approaches

Ham

Han

Kim

et al. 2019

Sensors

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“…Applications of PM to rolling stock systems have often analysed and targeted a single subsystem rather than a holistic perspective of the overall system (air conditioning system 5 and door systems 6 ). We also point to the reader to Giacco 7 for a longer discussion of maintenance issues in rolling stock.…”

Section: Literature Review On Pmmentioning

confidence: 99%

Optimizing preventive maintenance policy: A data-driven application for a light rail braking system

Corman

Kraijema

Godjevac

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part O:

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This article presents a case study determining the optimal preventive maintenance policy for a light rail rolling stock system in terms of reliability, availability, and maintenance costs. The maintenance policy defines one of the three predefined preventive maintenance actions at fixed time-based intervals for each of the subsystems of the braking system. Based on work, maintenance, and failure data, we model the reliability degradation of the system and its subsystems under the current maintenance policy by a Weibull distribution. We then analytically determine the relation between reliability, availability, and maintenance costs. We validate the model against recorded reliability and availability and get further insights by a dedicated sensitivity analysis. The model is then used in a sequential optimization framework determining preventive maintenance intervals to improve on the key performance indicators. We show the potential of data-driven modelling to determine optimal maintenance policy: same system availability and reliability can be achieved with 30% maintenance cost reduction, by prolonging the intervals and re-grouping maintenance actions.

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“…The DT's models need to be updated as often as possible to reliably reflect the system's behavior. Parameter [9][10][11] and state estimation strategies [12][13][14] are attractive solutions to keep the DT updated. However, such strategies are not necessarily compatible with the high-fidelity models often required to address all the physics of interest.…”

Section: Introductionmentioning

confidence: 99%

A Digital Twin for Operational Evaluation of Vertical Transportation Systems

et al. 2020

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Use of parameter estimation for the detection and diagnosis of faults on electric train door systems

Cited by 14 publications

References 9 publications

A Comparative Study of Fault Diagnosis for Train Door System: Traditional versus Deep Learning Approaches

A Comparative Study of Fault Diagnosis for Train Door System: Traditional versus Deep Learning Approaches

Optimizing preventive maintenance policy: A data-driven application for a light rail braking system

A Digital Twin for Operational Evaluation of Vertical Transportation Systems

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