Condition monitoring of railway electric point machines

Oyebande, B.O.; Renfrew, A.C.

doi:10.1049/ip-epa:20020499

Cited by 20 publications

(20 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors carried out a full appraisal of laboratory and trackside results and have presented these in reference [7]. The following analyses were executed.…”

Section: Analysis Of Resultsmentioning

confidence: 99%

“…The authors also gratefully acknowledge permission given by the Managing Director, Publishing and Information Services, Institution of Electrical Engineers for the use of Figs 7 and 13, originally presented as results in reference [7].…”

Section: Acknowledgementsmentioning

confidence: 99%

“…The research in this paper has been based on a traditional point machine design, previous strategy being to over-engineer components, sacrificing first cost and efficiency to achieve reliability. To ensure these objectives, a condition monitoring system would appear to offer substantial benefits to a railway infrastructure operator and a technique to provide this by monitoring only electrical quantities has been developed [7]. This avoids potential problems with mechanical monitoring in an environment where high shocks are encountered.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The design and implementation of a simulated trackside environment test system for an electric point machine

Oyebande¹,

Renfrew

2007

Proceedings of the Institution of Mechanical Engineers, Part F:

View full text Add to dashboard Cite

The background and constraints involved in condition monitoring of railway point operation are reviewed and discussed. An approximate method to replicate maladjustment, based on industry experience, is proposed and a computer simulation of the point machine and load is introduced. In view of uncertainty in results, a mechanical solution was preferred and the design and implementation of a simulated trackside environment test system and the instrumentation and measurement techniques used to gather practical data are described. In view of the severe railway environment, only electrical quantities were monitored using non-invasive sensors. A comparison is made with site data. Conclusions include the estimated payback available from implementing the condition monitoring system.

show abstract

“…The authors carried out a full appraisal of laboratory and trackside results and have presented these in reference [7]. The following analyses were executed.…”

Section: Analysis Of Resultsmentioning

confidence: 99%

Section: Acknowledgementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The design and implementation of a simulated trackside environment test system for an electric point machine

Oyebande¹,

Renfrew

2007

Proceedings of the Institution of Mechanical Engineers, Part F:

View full text Add to dashboard Cite

show abstract

“…This type of point mechanism was also studied by Fry (1999) using pressure transducers to monitor the pressure characteristics and pump activity. The method used by Oyebande and Renfrew (2002) primarily analysed the power spectral density and net energy of the voltage, motor speed, switchblade positions and its movement signals. Abed et al (1998) and Roberts and Chen (2005) investigated Single Throw Mechanical Equipment (STME).…”

Section: Monitoring Systemmentioning

confidence: 99%

An algorithmic approach for maintenance management based on advanced state space systems and harmonic regressions

2008

View full text Add to dashboard Cite

Point mechanisms are special track elements which failures results in delays and increased operating costs. In some cases such failures cause fatalities. A new robust algorithm for fault detection of point mechanisms is developed. It detects faults by comparing what can be considered the 'normal' or 'expected' shape of some signal with respect to the actual shape observed as new data become available. The expected shape is computed as a forecast of a combination of models. The proposed system deals with complicated features of the data in the case study, the main ones being the irregular sampling interval of the data and the time varying nature of the periodic behaviour. The system models are set up in a continuous-time framework and the system has been tested on a large dataset taken from a point mechanism operating on a commercial line.There have been many advances in rail-based transport over the past few years, such as higher speeds, increasing numbers of trains, shorter intervals between trains, and greater axle loads. All these changes result in the need for higher track quality standards and reduced maintenance time. The most important performance characteristics required are increased safety, greater reliability and reduced maintenance costs. Today, improvements are

show abstract

“…Forecasting the failures is a critical step in predictive maintenance. Some research has been conducted on this topic [4,7,23,32,38]. Delicate sensors usually serve as data collectors for voltages, currents and forces in related work.…”

Section: Introductionmentioning

confidence: 99%

Sample Adaptive Multiple Kernel Learning for Failure Prediction of Railway Points

Zhang

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

View full text Add to dashboard Cite

Railway points are among the key components of railway infrastructure. As a part of signal equipment, points control the routes of trains at railway junctions, having a significant impact on the reliability, capacity, and punctuality of rail transport. Meanwhile, they are also one of the most fragile parts in railway systems. Points failures cause a large portion of railway incidents. Traditionally, maintenance of points is based on a fixed time interval or raised after the equipment failures. Instead, it would be of great value if we could forecast points' failures and take action beforehand, minimising any negative effect. To date, most of the existing prediction methods are either lab-based or relying on specially installed sensors which makes them infeasible for large-scale implementation. Besides, they often use data from only one source. We, therefore, explore a new way that integrates multi-source data which are ready to hand to fulfil this task. We conducted our case study based on Sydney Trains rail network which is an extensive network of passenger and freight railways. Unfortunately, the real-world data are usually incomplete due to various reasons, e.g., faults in the database, operational errors or transmission faults. Besides, railway points differ in their locations, types and some other properties, which means it is hard to use a unified model to predict their failures. Aiming at this challenging task, we firstly constructed a dataset from multiple sources and selected key features with the help of domain experts. In this paper, we formulate our prediction task as a multiple kernel learning problem with missing kernels. We present a robust multiple kernel learning algorithm for predicting points failures. Our model takes into account the missing pattern of data as well as the inherent variance on different sets of railway points. Extensive experiments demonstrate the superiority of our algorithm compared with other state-of-the-art methods.

show abstract

Condition monitoring of railway electric point machines

Cited by 20 publications

References 3 publications

The design and implementation of a simulated trackside environment test system for an electric point machine

The design and implementation of a simulated trackside environment test system for an electric point machine

An algorithmic approach for maintenance management based on advanced state space systems and harmonic regressions

Sample Adaptive Multiple Kernel Learning for Failure Prediction of Railway Points

Contact Info

Product

Resources

About