Performance Monitoring for Vehicle Suspension System via Fuzzy Positivistic C-Means Clustering Based on Accelerometer Measurements
Top Cited Papers
- 13 October 2014
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE/ASME Transactions on Mechatronics
- Vol. 20 (5), 2613-2620
- https://doi.org/10.1109/tmech.2014.2358674
Abstract
This paper focuses on fault detection and isolation for vehicle suspension systems. The proposed method is divided into three steps: 1) confirming the number of clusters based on principal component analysis; 2) detecting faults by fuzzy positivistic C-means clustering and fault lines; and 3) isolating the root causes for faults by utilizing the Fisher discriminant analysis technique. Different from other schemes, this method only needs measurements of accelerometers that are fixed on the four corners of a vehicle suspension. Besides, different spring attenuation coefficients are regarded as a special failure instead of several ones. A full vehicle benchmark is applied to demonstrate the effectiveness of the method.Keywords
Funding Information
- State Key Laboratory of Robotics and System (HIT) (SKLRS-2014-MS-01)
- National Natural Science Foundation of China (61304102, 61472104)
- China Postdoctoral Science Foundation (2014T70339)
This publication has 33 references indexed in Scilit:
- Data-driven fault diagnosis for an automobile suspension system by using a clustering based methodJournal of the Franklin Institute, 2014
- Fault detection filter design for discrete-time nonlinear systems— A mixed optimizationSystems & Control Letters, 2014
- Robust PLS approach for KPI-related prediction and diagnosis against outliers and missing dataInternational Journal of Systems Science, 2014
- Data-driven Design of Fault Diagnosis and Fault-tolerant Control SystemsAdvances in Industrial Control, 2014
- Model-Based and Data-Driven Fault Detection Performance for a Small UAVIEEE/ASME Transactions on Mechatronics, 2013
- A novel aircraft fault diagnosis and prognosis system based on Gaussian Mixture ModelsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- Adaptive Backstepping Control for Active Suspension Systems With Hard ConstraintsIEEE/ASME Transactions on Mechatronics, 2012
- Fault Detection of Railway Vehicle Suspension Systems Using Multiple-Model ApproachJournal of Mechanical Systems for Transportation and Logistics, 2008
- Estimation of railway vehicle suspension parameters for condition monitoringControl Engineering Practice, 2007
- Variable reconstruction and sensor fault identification using canonical variate analysisJournal of Process Control, 2006