An efficient condition monitoring strategy of railway vehicle suspension based on recursive least-square algorithm

X Liu, Stefano Alfi, Stefano Bruni

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a model-based strategy for condition monitoring of suspensions in a railway bogie. This approach is based on recursive least-square (RLS) algorithm focusing on the ‘Input-output’ model. RLS is able to identify the unknown parameters from a noisy input-output system by memorizing the correlation properties. The identification of the suspension parameter is achieved by establishing the relationship between the excitation and response of a bogie. A fault detection method for vertical primary suspensions of one bogie is illustrated as an example of this scheme. Numerical simulation results from the rail vehicle dynamics software ‘ADTreS’ are utilized as ‘virtual measurements’, considering a trailed car of Italian ETR500 high-speed train. The test data from an E464 locomotive are also employed to validate the feasibility of this strategy for the real situation. Results of the parameter identification performed indicate that estimated suspension parameters are consistent or approximate with the values for reference, thereby supporting the application of this fault diagnosis technique to the future condition monitoring system of the rail vehicle suspension.
Original languageEnglish
Title of host publicationThe Dynamics of Vehicles on Roads and Tracks
Subtitle of host publicationProceedings of the 24th Symposium of the International Association for Vehicle System Dynamics (IAVSD 2015)
EditorsMartin Rosenberger, Manfred Plöchl, Klaus Six, Johannes Edelmann
PublisherCRC Press/Balkema
Pages861-870
Number of pages10
ISBN (Electronic)9781498777025
ISBN (Print)9781138028852
DOIs
Publication statusPublished - 3 May 2016
Externally publishedYes
Event24th Symposium of the International Association for Vehicle System Dynamics - Graz, Austria
Duration: 17 Aug 201521 Aug 2015
Conference number: 24

Conference

Conference24th Symposium of the International Association for Vehicle System Dynamics
Abbreviated titleIAVSD 2015
CountryAustria
CityGraz
Period17/08/1521/08/15

Fingerprint

Vehicle suspensions
Condition monitoring
Rails
Locomotives
Fault detection
Failure analysis
Identification (control systems)
Railroad cars
Computer simulation

Cite this

Liu, X., Alfi, S., & Bruni, S. (2016). An efficient condition monitoring strategy of railway vehicle suspension based on recursive least-square algorithm. In M. Rosenberger, M. Plöchl, K. Six, & J. Edelmann (Eds.), The Dynamics of Vehicles on Roads and Tracks: Proceedings of the 24th Symposium of the International Association for Vehicle System Dynamics (IAVSD 2015) (pp. 861-870). CRC Press/Balkema. https://doi.org/10.1201/b21185-92
Liu, X ; Alfi, Stefano ; Bruni, Stefano. / An efficient condition monitoring strategy of railway vehicle suspension based on recursive least-square algorithm. The Dynamics of Vehicles on Roads and Tracks: Proceedings of the 24th Symposium of the International Association for Vehicle System Dynamics (IAVSD 2015). editor / Martin Rosenberger ; Manfred Plöchl ; Klaus Six ; Johannes Edelmann. CRC Press/Balkema, 2016. pp. 861-870
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title = "An efficient condition monitoring strategy of railway vehicle suspension based on recursive least-square algorithm",
abstract = "This paper presents a model-based strategy for condition monitoring of suspensions in a railway bogie. This approach is based on recursive least-square (RLS) algorithm focusing on the ‘Input-output’ model. RLS is able to identify the unknown parameters from a noisy input-output system by memorizing the correlation properties. The identification of the suspension parameter is achieved by establishing the relationship between the excitation and response of a bogie. A fault detection method for vertical primary suspensions of one bogie is illustrated as an example of this scheme. Numerical simulation results from the rail vehicle dynamics software ‘ADTreS’ are utilized as ‘virtual measurements’, considering a trailed car of Italian ETR500 high-speed train. The test data from an E464 locomotive are also employed to validate the feasibility of this strategy for the real situation. Results of the parameter identification performed indicate that estimated suspension parameters are consistent or approximate with the values for reference, thereby supporting the application of this fault diagnosis technique to the future condition monitoring system of the rail vehicle suspension.",
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Liu, X, Alfi, S & Bruni, S 2016, An efficient condition monitoring strategy of railway vehicle suspension based on recursive least-square algorithm. in M Rosenberger, M Plöchl, K Six & J Edelmann (eds), The Dynamics of Vehicles on Roads and Tracks: Proceedings of the 24th Symposium of the International Association for Vehicle System Dynamics (IAVSD 2015). CRC Press/Balkema, pp. 861-870, 24th Symposium of the International Association for Vehicle System Dynamics, Graz, Austria, 17/08/15. https://doi.org/10.1201/b21185-92

An efficient condition monitoring strategy of railway vehicle suspension based on recursive least-square algorithm. / Liu, X; Alfi, Stefano; Bruni, Stefano.

The Dynamics of Vehicles on Roads and Tracks: Proceedings of the 24th Symposium of the International Association for Vehicle System Dynamics (IAVSD 2015). ed. / Martin Rosenberger; Manfred Plöchl; Klaus Six; Johannes Edelmann. CRC Press/Balkema, 2016. p. 861-870.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - This paper presents a model-based strategy for condition monitoring of suspensions in a railway bogie. This approach is based on recursive least-square (RLS) algorithm focusing on the ‘Input-output’ model. RLS is able to identify the unknown parameters from a noisy input-output system by memorizing the correlation properties. The identification of the suspension parameter is achieved by establishing the relationship between the excitation and response of a bogie. A fault detection method for vertical primary suspensions of one bogie is illustrated as an example of this scheme. Numerical simulation results from the rail vehicle dynamics software ‘ADTreS’ are utilized as ‘virtual measurements’, considering a trailed car of Italian ETR500 high-speed train. The test data from an E464 locomotive are also employed to validate the feasibility of this strategy for the real situation. Results of the parameter identification performed indicate that estimated suspension parameters are consistent or approximate with the values for reference, thereby supporting the application of this fault diagnosis technique to the future condition monitoring system of the rail vehicle suspension.

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Liu X, Alfi S, Bruni S. An efficient condition monitoring strategy of railway vehicle suspension based on recursive least-square algorithm. In Rosenberger M, Plöchl M, Six K, Edelmann J, editors, The Dynamics of Vehicles on Roads and Tracks: Proceedings of the 24th Symposium of the International Association for Vehicle System Dynamics (IAVSD 2015). CRC Press/Balkema. 2016. p. 861-870 https://doi.org/10.1201/b21185-92