A Software Sensor for Motorcycle Suspension Stroke

Electronic instrumentation and sensors are playing a relevant role in preventing and detecting road accidents, and improving the overall driving experience. Consequently fault detection on vehicle instrumentation and sensors also emerged as a main topic for its direct impact on cost and road safety. As solution, the employment of the analytical redundancy of measurement information is particularly suitable and/or necessary. This paper is about the estimation of suspension stroke exploiting the analytical redundancy among the measurement signals provided by a typically adopted instrumentation set. A software sensor for the rear suspension is designed according to a systematic approach, which is focused on recurrent Artificial Neural Networks able to predict the dynamic behavior of the vehicle. Experimental results show the rear suspension elongation can be correctly estimated. They disclose the possibility of setting-up an effective Instrument Fault Detection and Isolation scheme based on the real-time adoption of the proposed software sensor in order to improve the system