Operational Modal Analysis of Y25 Bogie via Stochastic Subspace Identification for the Condition Monitoring of Primary Suspension Systems

Fulong Liu, Jiongqi Wang, Miaoshuo Li, Fengshou Gu, Andrew D. Ball

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

Abstract

Railway vehicle suspension systems are vital to the vehicle safety and ride comfort, which is further driven by high speed operations. Condition Monitoring (CM) based online measurement is an efficient and achievable method to ensure the suspension systems working under normal function. In this paper, a potential method, which can achieve online CM of railway vehicle primary suspension, denoted as Average Correlation Signals based Stochastic Subspace Identification (ACS-SSI) was explored through simulation and experimental studies. Particularly, the dynamic performance of an Y25 bogie were investigated under the operational condition and the main focus was on the modes related to the suspension system. Firstly, ACS-SSI was presented briefly. Then, the employed test rig, an advanced dynamic test cell in the Institute of Railway Research (IRR) at University of Huddersfield, was introduced and the theoretical modal parameters of the tested bogie associating with the primary suspension system were calculated based on a multi rigid body model in the SIMPACK. The theoretical natural frequencies of bounce, roll and pitch modes are 11.07 Hz, 13.93 Hz and 15.19 Hz, respectively. Finally, ACS-SSI was adopted to identify modal parameters of the bogie using the collected responses on the four corners of the bogie frame. The pitch mode was identified successfully, which can illustrate the condition of the suspension system. Therefore, it can draw the conclusion that ACS-SSI has the potential to achieve suspension online monitoring.

LanguageEnglish
Title of host publicationProceedings of the 13th International Conference on Damage Assessment of Structures
EditorsMagd Abdel Wahab
PublisherSpringer
Pages166-181
Number of pages16
ISBN (Electronic)9789811383311
ISBN (Print)9789811383304
DOIs
Publication statusPublished - 2019
Event13th International Conference on Damage Assessment of Structures, DAMAS 2019 - Porto, Portugal
Duration: 9 Jul 201910 Jul 2019

Publication series

NameLecture Notes in Mechanical Engineering
ISSN (Print)2195-4356
ISSN (Electronic)2195-4364

Conference

Conference13th International Conference on Damage Assessment of Structures, DAMAS 2019
CountryPortugal
CityPorto
Period9/07/1910/07/19

Fingerprint

Condition monitoring
Modal analysis
Suspensions
Vehicle suspensions
Natural frequencies
Monitoring

Cite this

Liu, F., Wang, J., Li, M., Gu, F., & Ball, A. D. (2019). Operational Modal Analysis of Y25 Bogie via Stochastic Subspace Identification for the Condition Monitoring of Primary Suspension Systems. In M. A. Wahab (Ed.), Proceedings of the 13th International Conference on Damage Assessment of Structures (pp. 166-181). (Lecture Notes in Mechanical Engineering). Springer. https://doi.org/10.1007/978-981-13-8331-1_12
Liu, Fulong ; Wang, Jiongqi ; Li, Miaoshuo ; Gu, Fengshou ; Ball, Andrew D. / Operational Modal Analysis of Y25 Bogie via Stochastic Subspace Identification for the Condition Monitoring of Primary Suspension Systems. Proceedings of the 13th International Conference on Damage Assessment of Structures. editor / Magd Abdel Wahab. Springer, 2019. pp. 166-181 (Lecture Notes in Mechanical Engineering).
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title = "Operational Modal Analysis of Y25 Bogie via Stochastic Subspace Identification for the Condition Monitoring of Primary Suspension Systems",
abstract = "Railway vehicle suspension systems are vital to the vehicle safety and ride comfort, which is further driven by high speed operations. Condition Monitoring (CM) based online measurement is an efficient and achievable method to ensure the suspension systems working under normal function. In this paper, a potential method, which can achieve online CM of railway vehicle primary suspension, denoted as Average Correlation Signals based Stochastic Subspace Identification (ACS-SSI) was explored through simulation and experimental studies. Particularly, the dynamic performance of an Y25 bogie were investigated under the operational condition and the main focus was on the modes related to the suspension system. Firstly, ACS-SSI was presented briefly. Then, the employed test rig, an advanced dynamic test cell in the Institute of Railway Research (IRR) at University of Huddersfield, was introduced and the theoretical modal parameters of the tested bogie associating with the primary suspension system were calculated based on a multi rigid body model in the SIMPACK. The theoretical natural frequencies of bounce, roll and pitch modes are 11.07 Hz, 13.93 Hz and 15.19 Hz, respectively. Finally, ACS-SSI was adopted to identify modal parameters of the bogie using the collected responses on the four corners of the bogie frame. The pitch mode was identified successfully, which can illustrate the condition of the suspension system. Therefore, it can draw the conclusion that ACS-SSI has the potential to achieve suspension online monitoring.",
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Liu, F, Wang, J, Li, M, Gu, F & Ball, AD 2019, Operational Modal Analysis of Y25 Bogie via Stochastic Subspace Identification for the Condition Monitoring of Primary Suspension Systems. in MA Wahab (ed.), Proceedings of the 13th International Conference on Damage Assessment of Structures. Lecture Notes in Mechanical Engineering, Springer, pp. 166-181, 13th International Conference on Damage Assessment of Structures, DAMAS 2019, Porto, Portugal, 9/07/19. https://doi.org/10.1007/978-981-13-8331-1_12

Operational Modal Analysis of Y25 Bogie via Stochastic Subspace Identification for the Condition Monitoring of Primary Suspension Systems. / Liu, Fulong; Wang, Jiongqi; Li, Miaoshuo; Gu, Fengshou; Ball, Andrew D.

Proceedings of the 13th International Conference on Damage Assessment of Structures. ed. / Magd Abdel Wahab. Springer, 2019. p. 166-181 (Lecture Notes in Mechanical Engineering).

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

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AU - Ball, Andrew D.

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AB - Railway vehicle suspension systems are vital to the vehicle safety and ride comfort, which is further driven by high speed operations. Condition Monitoring (CM) based online measurement is an efficient and achievable method to ensure the suspension systems working under normal function. In this paper, a potential method, which can achieve online CM of railway vehicle primary suspension, denoted as Average Correlation Signals based Stochastic Subspace Identification (ACS-SSI) was explored through simulation and experimental studies. Particularly, the dynamic performance of an Y25 bogie were investigated under the operational condition and the main focus was on the modes related to the suspension system. Firstly, ACS-SSI was presented briefly. Then, the employed test rig, an advanced dynamic test cell in the Institute of Railway Research (IRR) at University of Huddersfield, was introduced and the theoretical modal parameters of the tested bogie associating with the primary suspension system were calculated based on a multi rigid body model in the SIMPACK. The theoretical natural frequencies of bounce, roll and pitch modes are 11.07 Hz, 13.93 Hz and 15.19 Hz, respectively. Finally, ACS-SSI was adopted to identify modal parameters of the bogie using the collected responses on the four corners of the bogie frame. The pitch mode was identified successfully, which can illustrate the condition of the suspension system. Therefore, it can draw the conclusion that ACS-SSI has the potential to achieve suspension online monitoring.

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M3 - Conference contribution

SN - 9789811383304

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BT - Proceedings of the 13th International Conference on Damage Assessment of Structures

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Liu F, Wang J, Li M, Gu F, Ball AD. Operational Modal Analysis of Y25 Bogie via Stochastic Subspace Identification for the Condition Monitoring of Primary Suspension Systems. In Wahab MA, editor, Proceedings of the 13th International Conference on Damage Assessment of Structures. Springer. 2019. p. 166-181. (Lecture Notes in Mechanical Engineering). https://doi.org/10.1007/978-981-13-8331-1_12