NARX ANN-based instrument fault detection in motorcycle

In the context of motorcycle, we can assist to an increasing interest toward semi-active suspension control systems able to improve both the comfort and the passenger’s safety in both racing and original equipment manufacturer applications. Such systems implement suitable strategies based on the measure of several quantities, among which the relative velocity of the wheels respect to the vehicle body with the aim of regulating in real-time the damping forces. The actual effectiveness of such strategy strongly depends on the reliability and accuracy of the data measured by the sensors involved in the control loop. Due to their simplicity and good performance in terms of linearity, the most used sensors for suspension displacement measurements are based on linear potentiometers but such kind of sensors suffer of wear and tear and aging higher than the other sensors involved in the control loop strategy. As a consequence, the fault detection of such sensor is strongly recommended to avoid wrong and in some cases dangerous suspension behaviors. To this aim, in this paper a Fault Detection scheme for the rear suspension stroke sensor is designed and verified. The residual generation is based on the use of a Nonlinear Auto-Regressive with eXogenous inputs (NARX) network which is able to effectively take into account for the system nonlinearity. Experimental results have proven the good promptness and reliability of the scheme in detecting different kind of faults as “un-calibration faults” (e.g. due to slight variations of the input/output sensor curve), “hold-faults” (e.g. due to the breaking of the potentiometer cursor), “open circuit” and “short circuit” (e.g. due to electrical interruptions and short circuits, respectively). In addition, to verify the feasibility of a real-time implementation on actual processing units employed in such context, the scheme has been successfully implemented on a microcontroller STM32 based on the general-purpose ARM-M4 architecture. The validation tests and analysis have shown that the proposed Instrument Fault Detection scheme could be successfully developed on these kind of architectures by assuring a real-time operating.