Condition monitoring of suspension components in railway bogies

Alfi, Stefano; Bionda, Stefano; Bruni, Stefano; Gasparetto, Livio

doi:10.1049/cp.2011.0613

Cited by 15 publications

(8 citation statements)

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“…The use of vertical axlebox accelerations is not addressed. Other studies delve on condition monitoring of suspension bogies, mainly for analysing vehicle performance [11].…”

Section: Introductionmentioning

confidence: 99%

Axlebox accelerations: Their acquisition and time–frequency characterisation for railway track monitoring purposes

et al. 2016

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Railway track maintenance is becoming a real challenge for Railway Engineers due to the need of meeting increasingly high quality requirements by means of cost-effective procedures. Frequently, this can be only achieved by implementing some technological developments from other fields into the railway sector, such as Digital Signal Processing. Indeed, the present work delves into data acquisition and processing techniques in order to enhance track surveying processes. For this purpose, run tests on the Metropolitan Rail Network of Valencia (Spain) were carried out, and axlebox accelerations were gathered and analysed in different ways. The results determined the optimal sampling and filtering frequencies as well as the location of accelerometers along the train. Furthermore, by means of spectral analysis and time-frequency representations, diverse track defects, track singularities and vibration modes can be clearly identified. It is shown how, with a Hamming time window of 0.5 s and an overlapping of 95%, a wide set of track defects can be detected, without the need of complementary analyses. These values yield the best results as they are a good compromise between time and frequency resolution and allow for appropriate pattern recognition of the corresponding track singularities and resonant frequencies.

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“…The use of vertical axlebox accelerations is not addressed. Other studies delve on condition monitoring of suspension bogies, mainly for analysing vehicle performance [11].…”

Section: Introductionmentioning

confidence: 99%

Axlebox accelerations: Their acquisition and time–frequency characterisation for railway track monitoring purposes

et al. 2016

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“…where X k and Y k are the state-space vector and the observation vector at step k, A first approach described in [16] is to consider the parameters of interest as statespace variables. An evolution equation must be defined for the parameters.…”

Section: Model-based Methods In the Context Of Train Suspension Monitmentioning

confidence: 99%

“…However, the simulataneous estimation of several parameters using EKF appears to achieve bad results. Consequently, in [16] they propose to use multiple EKF in parallel, each one of them performing the identification of a single suspension parameter. A modification of the train response can lead to changes in the estimated value of several parameters.…”

Section: Model-based Methods In the Context Of Train Suspension Monitmentioning

confidence: 99%

High-speed train suspension health monitoring using computational dynamics and acceleration measurements

Lebel

Soize

Funfschilling

et al. 2019

Vehicle System Dynamics

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This paper presents a novel method for the state health monitoring of high-speed train suspensions from in-line acceleration measurements by embedded sensors, for maintenance purposes. We propose a model-based method relying on a multibody simulation code. It performs the simultaneous identification of several suspension mechanical parameters. It is adapted to the introduction of uncertainties in the system and to the exploitation of numerous high-dimensional measurements. The novel method consists in a Bayesian calibration approach using a Gaussian process surrogate model of the likelihood function. The method has been validated on numerical experiments. We demonstrate its ability to detect evolutions of the health state of suspension elements. It has then been tested on actual acceleration measurements to study the time evolution of the suspension parameters.

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“…Detecting component faults at their early stages prevents further deterioration in vehicle performance and enhances safety. The rapid and effective monitoring techniques are essential to increase vehicle reliability and reduce maintenance cost [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…In comparison to other signal-based methods that use the absolute signals directly obtained by measuring the accelerations which are depending on the operating conditions [1,9,19,20], the cross-correlation method makes relative comparisons between measurements and hence reduces the effect of the factors that influence outputs.…”

Section: Introductionmentioning

confidence: 99%

Fault detection of damper in railway vehicle suspension based on the cross-correlation analysis of bogie accelerations

Dumitriu

2019

Mechanics & Industry

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Nowadays, the condition-based maintenance is associated more and more with railway transport to improve the safety, availability, reliability and capacity of this transport system, and to reduce life cycle costs for the railway vehicles. The condition-based maintenance requires that vehicle components are replaced based on their real condition, which implies the fault detection and isolation during the train's operation. The paper proposes a method to detect the failure of the damper in the primary suspension of the rail vehicle, based on the analysis of cross-correlation of the vertical accelerations measured on the bogie frame against the two axles. The numerical simulations and experimental results show a very good correlation between the bogie accelerations when the dampers are in a normal operation condition. This thing is shown based on the values of the cross-correlation coefficient (CCC) of the bogie accelerations. The failure in a damper can be detected by the decrease of the CCC of the bogie accelerations, a confirmed fact in the results derived from numerical simulations. The proposed method has more advantages, namely, it is a signal-based method and hence does not require a complex mathematical modelling of the vehicle-track system and knowledge of its parameters or of the external conditions; the method makes relative comparisons between measurements and hence reduces the effect of the factors that influence outputs; the method can be also extended for the secondary suspension; the method can be easily implemented on any type of bogie.

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Condition monitoring of suspension components in railway bogies

Cited by 15 publications

References 0 publications

Axlebox accelerations: Their acquisition and time–frequency characterisation for railway track monitoring purposes

Axlebox accelerations: Their acquisition and time–frequency characterisation for railway track monitoring purposes

High-speed train suspension health monitoring using computational dynamics and acceleration measurements

Fault detection of damper in railway vehicle suspension based on the cross-correlation analysis of bogie accelerations

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