An IFDI Scheme for Stroke Sensors in Motorcycle Semi-Active Suspension Systems

Systems designed to electronically control and to on-line adapt the suspension of motorcycles are today of great interest for motorcycle and after-market manufacturers. Indeed, semi-active suspension systems are able to drastically improve the comfort and the traction performance (which generally affects also the driver and the passenger safety) of motorcycle. The correct operating of such systems strongly depends on the reliability of data provided by the sensors involved in the measurement and control chain. In this framework, the paper proposes an Instrument Fault Detection and Isolation (IFDI) scheme, based on analytical redundancy, for the fault diagnosis of the front and rear stroke suspension sensors. As for the residuals generation, suitable mathematical links and soft sensors based on artificial neural networks are proposed. The design and the validation of the proposed IFDI scheme has been made by considering real data acquired by a motorcycle prototype equipped with a semi-active suspension system.