Condition monitoring of rail vehicle suspension based on recursive least-square algorithm

Xiaoyuan Liu, Stefano Alfi, S. Bruni

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

Abstract

This paper presents a model-based method 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 instead of the ‘State Space’ model. RLS estimates the unknown parameters from an input-output system by memorizing its 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 dampers 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. Results of the parameter identification performed on the virtual measurements indicate that estimated suspension parameters are consistent with the values adopted in the numerical simulations, thereby supporting the application of this technique for the fault detection and isolation to real cases.
Original languageEnglish
Title of host publicationProceedings of the 14th mini conference on vehicle system dynamics, identification and anomalies
PublisherBudapest University of Technology and Economics
Pages47-54
Number of pages8
ISBN (Print)9789633131862
Publication statusPublished - 2014
Externally publishedYes
Event14th Mini-Conference on Vehicle System Dynamics, Identification and Anomalies - Budapest, Hungary
Duration: 10 Nov 201412 Nov 2014
https://www.tib.eu/en/search/id/TIBKAT%3A862579252/Proceedings-of-the-14th-Mini-Conference-on-Vehicle/ (Link to Conference Details)

Conference

Conference14th Mini-Conference on Vehicle System Dynamics, Identification and Anomalies
Abbreviated titleVSDIA 2014
CountryHungary
CityBudapest
Period10/11/1412/11/14
Internet address

Fingerprint

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

Cite this

Liu, X., Alfi, S., & Bruni, S. (2014). Condition monitoring of rail vehicle suspension based on recursive least-square algorithm. In Proceedings of the 14th mini conference on vehicle system dynamics, identification and anomalies (pp. 47-54). Budapest University of Technology and Economics.
Liu, Xiaoyuan ; Alfi, Stefano ; Bruni, S. / Condition monitoring of rail vehicle suspension based on recursive least-square algorithm. Proceedings of the 14th mini conference on vehicle system dynamics, identification and anomalies. Budapest University of Technology and Economics, 2014. pp. 47-54
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abstract = "This paper presents a model-based method 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 instead of the ‘State Space’ model. RLS estimates the unknown parameters from an input-output system by memorizing its 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 dampers 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. Results of the parameter identification performed on the virtual measurements indicate that estimated suspension parameters are consistent with the values adopted in the numerical simulations, thereby supporting the application of this technique for the fault detection and isolation to real cases.",
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Liu, X, Alfi, S & Bruni, S 2014, Condition monitoring of rail vehicle suspension based on recursive least-square algorithm. in Proceedings of the 14th mini conference on vehicle system dynamics, identification and anomalies. Budapest University of Technology and Economics, pp. 47-54, 14th Mini-Conference on Vehicle System Dynamics, Identification and Anomalies, Budapest, Hungary, 10/11/14.

Condition monitoring of rail vehicle suspension based on recursive least-square algorithm. / Liu, Xiaoyuan; Alfi, Stefano; Bruni, S.

Proceedings of the 14th mini conference on vehicle system dynamics, identification and anomalies. Budapest University of Technology and Economics, 2014. p. 47-54.

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

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AB - This paper presents a model-based method 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 instead of the ‘State Space’ model. RLS estimates the unknown parameters from an input-output system by memorizing its 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 dampers 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. Results of the parameter identification performed on the virtual measurements indicate that estimated suspension parameters are consistent with the values adopted in the numerical simulations, thereby supporting the application of this technique for the fault detection and isolation to real cases.

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Liu X, Alfi S, Bruni S. Condition monitoring of rail vehicle suspension based on recursive least-square algorithm. In Proceedings of the 14th mini conference on vehicle system dynamics, identification and anomalies. Budapest University of Technology and Economics. 2014. p. 47-54