Distributed quantitative and qualitative fault diagnosis: railway junction case study

Roberts, Clive; Dassanayake, H. P. B.; Lehrasab, N.; Goodman, C.J.

doi:10.1016/s0967-0661(01)00159-9

Cited by 55 publications

(26 citation statements)

References 5 publications

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“…Following successful implementation on a level crossing mechanism (Roberts 2002) [23], the authors adapted the methods to detect faults in seven point machines at Abbotswood junction, shown in Fig. 6 as boxes 638, 639, 640, 641A, 641B, 642A and 642B.…”

Section: Device and Datamentioning

confidence: 99%

Digital Filters for Maintenance Management

Márquez¹,

Pedregal²

2011

Digital Filters

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Faults in mechanisms must be detected quickly and reliably in order to avoid important losses. Detection systems should be developed to minimize maintenance costs and are generally based on consistent models, but as simple as possible. Also, the models for detecting faults must adapt to external and internal conditions to the mechanism. The present chapter deals with three particular maintenance algorithms for turnouts in railway infrastructure by means of discrete filters that comply with these general objectives. All of them have the virtue of being developed within a well-known and common framework, namely the State Space with the help of the Kalman Filter (KF) and/or complementary Fixed Interval Smoother (FIS) algorithms. The algorithms are tested on real applications and thorough results are shown. IntroductionFaults in any important mechanisms must be detected quickly and reliably if the information is to be useful. Generally such mechanisms may be modeled as discrete dynamic systems, where data must be processed on line. When feasible, the detection system should use a model as simple as possible for detecting faults quickly by analyzing data in real time. The models for detecting faults must adapt to external and internal conditions to the mechanism, since both of them may affect the system as a whole.The present chapter deals with maintenance systems for turnouts in railway infrastructure by means of discrete filters. Turnouts are assembled from switches and a crossing where the moving parts are often described as the "points" move by the point mechanism. The standard railway point mechanism is a complex electro-mechanical device with many potential failure modes.Several approaches for maintenance of such devices are shown in this chapter and briefly described in this introduction. All of them have the virtue of being developed within a wellknown common framework, namely the State Space (SS) with the help of the Kalman Filter (KF) and/or complementary Fixed Interval Smoother (FIS) algorithms, exposed in general terms in the following section. Based on this common framework, the following subsections in this introduction show the particular applications shown in later sections of the chapter. Filtering with Integrated Random Walks (IRW)One possible way to analyze faults on line is to work with a reference dynamic system for their analysis. If the absolute value of the difference between the actual data and the reference data (i.e. the profile without any fault) is analyzed, the majority of faults may be detected by means of a simplified univariate dynamic system, like the one explored in [9]. The dynamic system and the use of the SS framework and the KF in this study allow increasing the reliability of the model presented that is the basic input to a rule-based decision mechanism. When applied to the linear discrete data filtering problem, the KF is a powerful algorithm, because it supports estimations of past, present and, most importantly, future states. It can therefore be used in predictive m...

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Section: Device and Datamentioning

confidence: 99%

Digital Filters for Maintenance Management

Márquez¹,

Pedregal²

2011

Digital Filters

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“…There are essentially two opposing attitudes for diagnosis system [22] and [27]. First one is corrective maintenance or diagnosis after the failure, second one is the preventive maintenance [28] or diagnosis before the failure.…”

Section: Introductionmentioning

confidence: 99%

Model Study and Fault Detection for the Railway System

Rawia¹,

Tlijani²,

Knani³

2017

ijacsa

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Abstract-The wheel-rail-sleepers system is simulated as a series of moving point loads on an Euler-Bernoulli beam resting on a visco-elastic half space. This paper concentrates on the railsleepers interaction system (railway system) and the fault detection. The main objective is to mathematically develop and implement a dynamic model of a railway system then the diagnosis of system defects using a Luenberger observer (LO). The simulation results are based on a physical description, mathematical equations and simulations with MATLAB simulation program.

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“…points, signals) is a vital issue due to its harsh results. Therefore, fault diagnosis and condition monitoring studies on railway point mechanisms can be found in the literature (Rouvray et al 1998;Roberts et al 2002;Garcia Marquez et al 2003;Zattoni 2006). However, these studies are addressed the fault diagnosis problem from a different perspective.…”

Section: Introductionmentioning

confidence: 99%

Modular Fault Diagnosis in Fixed-Block Railway Signaling Systems

et al. 2016

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Abstract:The diagnosis of possible faults in railway signaling systems is an important issue to provide safe travel and transportation in railways. Signaling system designers have to consider the possible faults which may occur in railway field components both on the requirements preparation phase and on the development phase of the signaling system software or namely, the interlocking system. Although the diagnosis of different unobservable faults is relatively hard, especially for large scale railway fields, this complexity can be overcome by using the Discrete Event System (DES) based modular diagnosis approach which is explained in this paper. The main advantage of using such modular approach for fault diagnosis in fixed-block signaling systems is the inspection of the diagnosability of the whole system with respect to its subsystems (railway field components). In this study, the diagnosability of the railway field equipment and the whole system is also explained with a case study.

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Distributed quantitative and qualitative fault diagnosis: railway junction case study

Cited by 55 publications

References 5 publications

Digital Filters for Maintenance Management

Digital Filters for Maintenance Management

Model Study and Fault Detection for the Railway System

Modular Fault Diagnosis in Fixed-Block Railway Signaling Systems

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