Correlation signal subset-based stochastic subspace identification for an online identification of railway vehicle suspension systems

ABSTRACT

Monitoring the condition of suspension systems is significant to ensure the safe operation of modern railway vehicles. For this purpose, an online modal identification scheme, denoted as Correlation Subset based Stochastic Subspace Identification (CoS-SSI) is proposed in this paper to monitor the suspension conditions. Because of the widespread of the dynamic contact status between wheel and track, especially under faulty suspension cases, the vibration responses measured online exhibit high nonstationarity and nonlinearity. To take into account these characteristics of signals, the input correlation signals for SSI are clustered into several successive subsets according to their magnitudes, on which SSI is implemented one by one. In this way it yields a magnitude adaptive SSI for more reliable and accurate identification. Experimental studies were conducted on a 1/5th scaled roller rig system to verify the effectiveness of the proposed method for suspension monitoring. The experimental results show that the CoS-SSI outperform the conventional SSI in that it produces more reliable and realistic identification for the nonlinear system. Furthermore, the effectiveness of the CoS-SSI was verified experimentally with two faulty suspension faults induced into the system.

KEYWORDS:

• Vehicle suspension
• online monitoring
• CoS-SSI
• nonstationary
• nonlinear system

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Fulong Liu http://orcid.org/0000-0003-1155-1759

Fengshou Gu http://orcid.org/0000-0003-4907-525X

Andrew D. Ball http://orcid.org/0000-0001-7540-8965

Additional information

Funding

The authors would like to thank the Hebei Provincial International Science and Technology Cooperation Program of China [grant number 17394303D], the National Natural Science Foundation of China [grant number 51605133; 51705127], China Scholarship Council [CSC grant number 201608060041] for sponsorship and the Open Research Fund of the Traction Power State Key Laboratory, Southwest Jiaotong University, China [grant number TPL1704] to carry out the research.