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  • Mechanical and production engineering [3]
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Use of real time creep force estimation data for assessment of low adhesion in the wheel/rail contact
Author(s): C.P. Ward, R.M. Goodall, R. Dixon, G. Charles
Source: 5th IET Conference on Railway Condition Monitoring and Non-Destructive Testing (RCM 2011), 2011, p. 82 - 82
Type: ConferencePaper
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Low adhesion in railway vehicle wheel/rail contact can create large cost and punctuality issues for train operators and users. There is currently a lack of real time knowledge of the running conditions on networks, therefore the work reported here proposes a method of detecting dynamically the adhesion conditions underneath a rail vehicle using modest cost sensors and advanced model based monitoring of the creep forces. The estimated creep forces are then post processed to give an estimation of the current adhesion condition. (6 pages)

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Condition Monitoring of Rail Vehicle Bogies
Author(s): C.P. Ward, R.M. Goodall, R. Dixon, G. Charles
Source: UKACC International Conference on CONTROL 2010, 2010, p. 1178 - 1183
Type: ConferencePaper
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This paper details a range of work carried out by the authors within the general theme of advanced condition monitoring possibilities for rail vehicle bogies. Maintenance of rail vehicle bogies represents one of the largest areas of whole vehicle running costs and their efficient operation is of safety critical importance to the entire rail system. Currently the majority of maintenance is carried out on a scheduled basis which can be time consuming, costly and potentially not effective at fault detection. This paper reviews concepts that could allow real time detection of the condition of the bogie so as to reduce vehicle out of service time and improve safe operation. Concepts reviewed are: the use of condition monitoring for detection of suspension component condition; detection of low adhesion conditions; and assessment of the wheel-rail interface condition.

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Wheel-Rail Profile Condition Monitoring
Author(s): C.P. Ward, R.M. Goodall, R. Dixon
Source:
Type: ConferencePaper
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Increased railway patronage worldwide is putting pressure on rolling stock and infrastructure to operate at higher capacity and with improved punctuality. Condition monitoring is seen as a contributing factor in enabling this and is highlighted here in the context of rolling stock being procured with high capacity data buses, multiple sensors and centralised control. This therefore leaves scope for advanced computational diagnostic concepts. The rail vehicle bogie and associated wheelsets are one of the largest and most costly areas of maintenance on rolling stock and presented here is a potential method for real time estimation of wheel-rail contact wear to move this currently scheduled based assessment to condition based assessment. This technique utilises recursive `grey box' least squares system identification, used in a piecewise linear manner, to capture the strongly discontinuous nonlinear nature of the wheel-rail geometry.

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