Fault diagnosis of an automotive suspension system

With the development of the embedded application and driving assistance systems, it becomes relevant to develop parallel mechanisms in order to check and to diagnose these new systems. In this thesis we focus our research on one of this type of parallel mechanisms and analytical redundancy for fault diagnosis of an automotive suspension system. We have considered a quarter model car passive suspension model and used a parameter estimation, ARX model, method to detect the fault happening in the damper and spring of system. Moreover, afterward we have deployed a neural network classifier to isolate the faults and identifies where the fault is happening. Then in this regard, the safety measurements and redundancies can take into the effect to prevent failure in the system. It is shown that The ARX estimator could quickly detect the fault online using the vertical acceleration and displacement sensor data which are common sensors in nowadays vehicles. Hence, the clear divergence is the ARX response make it easy to deploy a threshold to give alarm to the intelligent system of vehicle and the neural classifier can quickly show the place of fault occurrence.

Advanced Non-Destructive Inspections focused on composite materials application for the automotive and marine sectors

The goal of this master thesis is to explain in detail the application of Non-Destructive-Inspection on the Automotive and the Marine sectors. Nowadays, these two particular industries faces many challenges, including increased global competition, the need for higher performance, a reduction in costs and tighter environmental and safety requirements. The materials used for these applications play key roles in overcoming these challenges. So, also an NDI procedure need to be planned in order to avoid problems during the manufacturing process and the after sale life of the structures. The entire thesis work has been done in collaboration with Vetorix Engineering.