Nowadays, with the integration of diverse driver assistance systems the vehicle has become a complex mechatronic system. The goal of this improvement is to help a driver to manage his vehicle and to perceive his environment. The functional capability of these driver assistance systems is mostly influenced by the functionality of mechanical and/or hydraulic components in the vehicle and the sensors, which are essential elements for the estimation of external and internal state of the vehicle. To ensure the reliability of the vehicle there is a great need to develop a monitoring system to meet the new requirements. In a complex mechatronic system the functionality and reliability of the whole system depend not only on those of the subsystems. To monitor the functionality of a system, it is necessary to understand how the system works. Thus it becomes more difficult for the driver to monitor the functionality and the working state of modern vehicle. In order to help a driver to monitor the performance of his/her vehicle, an appropriate condition monitoring system is required. This paper outlines an approach to estimate the vehicle longitudinal performance for the purpose of developing a fault monitoring system based on simulation technique. Firstly, the necessity of this monitoring system is expounded. After introduction of diverse methods for the analysis of the system safety, structure of this monitoring system is introduced. The kernel of this system is a vehicle model and an observer model. Regarding the requirements of a real-time operating system, the vehicle model in this work is built by applying an artificial neural network (ANN). The achieved structure of the network and simulation results are presented in this paper. Furthermore the real-time capability of the networks is verified within this work.

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