A wayside inspection station [railways]

Smith, D.L.

doi:10.1109/rrcon.1998.668085

Cited by 5 publications

(6 citation statements)

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“…This classification looks as follows [9]: -deterministic methods e.g. [6], [7], [9], [10], [11], [13], -statistic methods e.g. [2], [3], -methods based on the artificial intelligence e.g.…”

Section: Problem Formulationmentioning

confidence: 99%

“…It identifies excitation forces by summing the acceleration responses with appropriate weights. In the time domain, one can identify the harmonic force by using the inversion of the parametric regressive model [7]. The time domain iterative methods deal with minimizing the quality coefficient which is defined as the difference between measured and estimated response of a system [9].…”

Section: Problem Formulationmentioning

confidence: 99%

“…Dynamic forces are generated by out-of-round wheels mainly. This phenomena can be a reason of crack of sleepers, damage the rail head, and cause failure of rail by either growth of rolling contact fatigue cracks, growth of detail fractures, or fatigue and fracture of in-track welds [7,8]. In the extreme, fracture of the rail can result in derailment.…”

Section: Introductionmentioning

confidence: 99%

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Rail Track and Rail Vehicle Intelligent Monitoring System

Uhl¹,

Mendrok²,

Chudzikiewicz³

2010

Archives of Transport

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Structural Health Monitoring is becoming more and more important for today's railway. Higher requirements are set for safety and availability of both trains and tracks, that can be achieved using new type of SHM systems. Proposed SHM system is based on vibration measurements during the rail vehicle operation simultaneously with GPS position and velocity estimation. Algorithm for rail track health assessment is formulated as inverse identification problem of dynamic parameters of rail-vehicle system. Track irregularities of tracks are identified using this procedure. The case study of the SHM system operates on rail vehicle is presented in the paper.

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“…This classification looks as follows [9]: -deterministic methods e.g. [6], [7], [9], [10], [11], [13], -statistic methods e.g. [2], [3], -methods based on the artificial intelligence e.g.…”

Section: Problem Formulationmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Rail Track and Rail Vehicle Intelligent Monitoring System

Uhl¹,

Mendrok²,

Chudzikiewicz³

2010

Archives of Transport

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“…There exists two common kinds of train bearing condition monitoring systems nowadays, one is the on train detection system (OTDS) and the other is the wayside acoustic defective bearing detector system (ADBD) [ 2 , 5 ]. Since its advent, several diagnostic strategies have been proposed for OTDS, such as oil monitoring [ 6 ], temperature detecting [ 7 , 8 ], vibration signal analysis [ 9 , 10 , 11 ], and acoustic emission diagnosis [ 12 , 13 , 14 ] methods. However, the oil monitoring strategy is only suitable for lubrication bearing in low velocity situations [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Online Doppler Effect Elimination Based on Unequal Time Interval Sampling for Wayside Acoustic Bearing Fault Detecting System

Ouyang

Zhang

et al. 2015

Sensors

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The railway occupies a fairly important position in transportation due to its high speed and strong transportation capability. As a consequence, it is a key issue to guarantee continuous running and transportation safety of trains. Meanwhile, time consumption of the diagnosis procedure is of extreme importance for the detecting system. However, most of the current adopted techniques in the wayside acoustic defective bearing detector system (ADBD) are offline strategies, which means that the signal is analyzed after the sampling process. This would result in unavoidable time latency. Besides, the acquired acoustic signal would be corrupted by the Doppler effect because of high relative speed between the train and the data acquisition system (DAS). Thus, it is difficult to effectively diagnose the bearing defects immediately. In this paper, a new strategy called online Doppler effect elimination (ODEE) is proposed to remove the Doppler distortion online by the introduced unequal interval sampling scheme. The steps of proposed strategy are as follows: The essential parameters are acquired in advance. Then, the introduced unequal time interval sampling strategy is used to restore the Doppler distortion signal, and the amplitude of the signal is demodulated as well. Thus, the restored Doppler-free signal is obtained online. The proposed ODEE method has been employed in simulation analysis. Ultimately, the ODEE method is implemented in the embedded system for fault diagnosis of the train bearing. The results are in good accordance with the bearing defects, which verifies the good performance of the proposed strategy.

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“…Already in 1998 Smith [3] describes a digitalized hot box detection system, which was able to reduce the generation of false alarms by means of statistical analysis and digital transmission. Moreover Smith allows integrating optional high, wide and dragging equipment detector systems.…”

Section: State Of the Artmentioning

confidence: 99%

Joining sensor systems for railway vehicle inspection

Maly

Rumpler²,

Schweinzer³

Proceedings of IEEE Sensors, 2004.

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In today's field of railway, a number of stand alone inspection systems are available and each of them addresses only particular train fault-states. Our work describes a concept for integration of these systems into a complete "checkpoint" system with regard to the specific application range of railway vehicle inspection 1 . In contrast to single fault-state detection systems, our concept of an overall system allows to combine sensor information and to evaluate train fault-states from a more global view. Hence, it provides an abstract data representation for establishing a common data base. To achieve enhanced information combining sensor data of asynchronously working sensor systems, a tight association between measurements and their particular measurement ranges on the train is an elementary functionality. As a crucial aspect, the accuracy of train fault estimation can be proved by the system. Furthermore, the ability of the overall system in characterizing fault-states will mostly be extended.

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A wayside inspection station [railways]

Cited by 5 publications

References 0 publications

Rail Track and Rail Vehicle Intelligent Monitoring System

Rail Track and Rail Vehicle Intelligent Monitoring System

Online Doppler Effect Elimination Based on Unequal Time Interval Sampling for Wayside Acoustic Bearing Fault Detecting System

Joining sensor systems for railway vehicle inspection

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