A Review in Fault Diagnosis and Health Assessment for Railway Traction Drives

Garramiola, Fernando; Poza, Javier; Madina, Patxi; Olmo, Jon del; Almandoz, Gaizka

doi:10.3390/app8122475

Cited by 24 publications

(13 citation statements)

References 87 publications

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“…The current DBN structure itself is not able to fully incorporate physical knowledge of the system, such as degradation and failure modes, into its diagnostic and prognostic assessments; as such, this system information might be a better alternative for substituting missing data with respect to nodes related to remaining useful life predictions. There has been a range of work in this subject for various systems, including wind turbines [26], railways [27], and subsea pipelines [28]; • A next-level line of study would be to vary the weight of information provided to the system as evidence. Currently, all information used by the model carries the same weight.…”

Section: Discussionmentioning

confidence: 99%

A Dynamic Bayesian Network Structure for Joint Diagnostics and Prognostics of Complex Engineering Systems

Lewis

Groth

2020

Algorithms

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Dynamic Bayesian networks (DBNs) represent complex time-dependent causal relationships through the use of conditional probabilities and directed acyclic graph models. DBNs enable the forward and backward inference of system states, diagnosing current system health, and forecasting future system prognosis within the same modeling framework. As a result, there has been growing interest in using DBNs for reliability engineering problems and applications in risk assessment. However, there are open questions about how they can be used to support diagnostics and prognostic health monitoring of a complex engineering system (CES), e.g., power plants, processing facilities and maritime vessels. These systems’ tightly integrated human, hardware, and software components and dynamic operational environments have previously been difficult to model. As part of the growing literature advancing the understanding of how DBNs can be used to improve the risk assessments and health monitoring of CESs, this paper shows the prognostic and diagnostic inference capabilities that are possible to encapsulate within a single DBN model. Using simulated accident sequence data from a model sodium fast nuclear reactor as a case study, a DBN is designed, quantified, and verified based on evidence associated with a transient overpower. The results indicate that a joint prognostic and diagnostic model that is responsive to new system evidence can be generated from operating data to represent CES health. Such a model can therefore serve as another training tool for CES operators to better prepare for accident scenarios.

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Section: Discussionmentioning

confidence: 99%

A Dynamic Bayesian Network Structure for Joint Diagnostics and Prognostics of Complex Engineering Systems

Lewis

Groth

2020

Algorithms

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“…Among the statistical approaches for fault evaluation, Ding presented the Generalized Likelihood Ratio (GLR) [31]. The GLR is based on the Likelihood Ratio (LR), which is based on the relation between two probability densities for a sample i of the signal r, one of probability densities for a healthy case and the other one for a faulty case, as it is shown in Equation (7). In case of a signal of n samples, the LR is given by Equation (8).…”

Section: Fault Evaluation Based On Generalized Likelihood Ratiomentioning

confidence: 99%

“…Furthermore, information related to severity and fault mode could be essential to get an optimal fault tolerant system, by means of parameters change or reconfigurations in an automatic way, for example by substituting the measured value by the estimated value [6]. Due to the importance of railway Traction Drive in the train availability, different FDI approaches have been implemented [7] in order to detect and isolate faults in sensors, electric machines and power converters. FDI approaches are mainly classified into model-based and data-driven techniques [8].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Sensor Fault Diagnosis for Maintenance in Railway Traction Drives

Garramiola

Poza

Madina

et al. 2020

Sensors

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Due to the importance of sensors in railway traction drives availability, sensor fault diagnosis has become a key point in order to move from preventive maintenance to condition-based maintenance. Most research works are limited to sensor fault detection and isolation, but only a few of them analyze the types of sensor faults, such as offset or gain, with the aim of reconfiguring the sensor in order to implement a fault tolerant system. This article is based on a fusion of model-based and data-driven techniques. First, an observer-based approach, using a Sliding Mode observer, is utilized for sensor fault reconstruction in real time. Then, once the fault is detected, a time window of sensor measurements and sensor fault reconstruction is sent to the remote maintenance center for fault evaluation. Finally, an offline processing is carried out to discriminate between gain and offset sensor faults, in order to get a maintenance decision-making to reconfigure the sensor during the next train stop. Fault classification is done by means of histograms and statistics. The technique here proposed is applied to the DC-link voltage sensor in a railway traction drive and is validated in a hardware-in-the-loop platform.Sensors 2020, 20, 962 2 of 21 which normally requires an important effort in complex systems, but as an advantage, it is suitable for on-board and real-time implementation, as it requires neither a large quantity of data, nor high computational resources. The model-based techniques can be divided into two parts: the residual generation and the residual evaluation. On the other hand, data-driven techniques do not need a knowledge of the physical system, but the computational burden is normally higher. Moreover, data quantity to process and storage is large, so it makes difficult its on-board implementation.

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“…The current approach for planning maintenance works on railway infrastructure is mostly reactive [2], since infrastructure managers usually do not have sufficient information and accurate models to assess and predict the condition. Forensic analyses of historic failures often reveal that indicators of distress were ignored due to lack of understanding or the absence of a proper framework for decision-making [3].…”

Section: Introductionmentioning

confidence: 99%

Categorization of the Condition of Railway Embankments Using a Multi-Attribute Utility Theory

et al. 2019

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In the current economic climate, it is crucial to optimize the use of all resources regarding railway infrastructure maintenance. In this paper, a multi-attribute decision support framework is applied to categorize railway embankments in order to prioritize maintenance activities. The paper describes a methodology to first determine the current condition of embankments using a combination of ground penetrating radar (GPR) surveys, visual inspection, and historical data about maintenance activities. These attributes are then used for the development of a multi-attribute utility theory model, which can be used as a support for decision making process for maintenance planning. The methodology is demonstrated for the categorization of 181 km of railway embankments in Croatia.

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A Review in Fault Diagnosis and Health Assessment for Railway Traction Drives

Cited by 24 publications

References 87 publications

A Dynamic Bayesian Network Structure for Joint Diagnostics and Prognostics of Complex Engineering Systems

A Dynamic Bayesian Network Structure for Joint Diagnostics and Prognostics of Complex Engineering Systems

A Hybrid Sensor Fault Diagnosis for Maintenance in Railway Traction Drives

Categorization of the Condition of Railway Embankments Using a Multi-Attribute Utility Theory

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