A validation study of ACS-SSI for online condition monitoring of vehicle suspension systems

Fulong Liu, Fengshou Gu, Yunshi Zhao, Andrew Ball

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Condition monitoring (CM) is an effective approach to prevent accidents caused by structural damage. An online condition monitoring system for suspension is vital to vehicles' safety and reliability, as suspension is an important subsystem of the vehicle. Average Correlation Signals based Stochastic Subspace Identification (ACS-SSI) is a new approach which has the potential to achieve online CM for vehicle suspension systems. In order to investigate the influences of possible errors, like placement of sensors and excitation amplitudes, on implementing ACS-SSI for online suspension CM, a simplified test device is developed to study the performance of identifying the most three common vibration modes of a vehicle, which are bounce, pitch and roll. A three degrees of freedom (3-DOF) model were established for the devices to highlight the effects of the errors. The study results show that the ACS-SSI is an effective method to carry out system identification even if the inputs are highly noisy and non-stationary. However, the implementation of ACS-SSI needs to take into account these potential errors in order to obtain accurate CM results.

Original languageEnglish
Pages (from-to)369-375
Number of pages7
JournalVibroengineering Procedia
Volume10
Publication statusPublished - 8 Dec 2016

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Vehicle suspensions
Condition monitoring
Identification (control systems)
Accidents
Sensors

Cite this

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abstract = "Condition monitoring (CM) is an effective approach to prevent accidents caused by structural damage. An online condition monitoring system for suspension is vital to vehicles' safety and reliability, as suspension is an important subsystem of the vehicle. Average Correlation Signals based Stochastic Subspace Identification (ACS-SSI) is a new approach which has the potential to achieve online CM for vehicle suspension systems. In order to investigate the influences of possible errors, like placement of sensors and excitation amplitudes, on implementing ACS-SSI for online suspension CM, a simplified test device is developed to study the performance of identifying the most three common vibration modes of a vehicle, which are bounce, pitch and roll. A three degrees of freedom (3-DOF) model were established for the devices to highlight the effects of the errors. The study results show that the ACS-SSI is an effective method to carry out system identification even if the inputs are highly noisy and non-stationary. However, the implementation of ACS-SSI needs to take into account these potential errors in order to obtain accurate CM results.",
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A validation study of ACS-SSI for online condition monitoring of vehicle suspension systems. / Liu, Fulong; Gu, Fengshou; Zhao, Yunshi; Ball, Andrew.

In: Vibroengineering Procedia, Vol. 10, 08.12.2016, p. 369-375.

Research output: Contribution to journalArticle

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AU - Liu, Fulong

AU - Gu, Fengshou

AU - Zhao, Yunshi

AU - Ball, Andrew

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N2 - Condition monitoring (CM) is an effective approach to prevent accidents caused by structural damage. An online condition monitoring system for suspension is vital to vehicles' safety and reliability, as suspension is an important subsystem of the vehicle. Average Correlation Signals based Stochastic Subspace Identification (ACS-SSI) is a new approach which has the potential to achieve online CM for vehicle suspension systems. In order to investigate the influences of possible errors, like placement of sensors and excitation amplitudes, on implementing ACS-SSI for online suspension CM, a simplified test device is developed to study the performance of identifying the most three common vibration modes of a vehicle, which are bounce, pitch and roll. A three degrees of freedom (3-DOF) model were established for the devices to highlight the effects of the errors. The study results show that the ACS-SSI is an effective method to carry out system identification even if the inputs are highly noisy and non-stationary. However, the implementation of ACS-SSI needs to take into account these potential errors in order to obtain accurate CM results.

AB - Condition monitoring (CM) is an effective approach to prevent accidents caused by structural damage. An online condition monitoring system for suspension is vital to vehicles' safety and reliability, as suspension is an important subsystem of the vehicle. Average Correlation Signals based Stochastic Subspace Identification (ACS-SSI) is a new approach which has the potential to achieve online CM for vehicle suspension systems. In order to investigate the influences of possible errors, like placement of sensors and excitation amplitudes, on implementing ACS-SSI for online suspension CM, a simplified test device is developed to study the performance of identifying the most three common vibration modes of a vehicle, which are bounce, pitch and roll. A three degrees of freedom (3-DOF) model were established for the devices to highlight the effects of the errors. The study results show that the ACS-SSI is an effective method to carry out system identification even if the inputs are highly noisy and non-stationary. However, the implementation of ACS-SSI needs to take into account these potential errors in order to obtain accurate CM results.

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