Fault diagnosis of an automotive suspension system

Fani, Mehran (2016) Fault diagnosis of an automotive suspension system. [Laurea magistrale], Università di Bologna, Corso di Studio in Ingegneria dell'automazione [LM-DM270], Documento full-text non disponibile
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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.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Fani, Mehran
Relatore della tesi
Correlatore della tesi
Corso di studio
Curriculum: Automation engineering
Ordinamento Cds
Parole chiave
Fault,diagnosis,detection,Automotive,Suspension,Neural network,neural classifier,parameter,estimation,Arx model,quarter car model
Data di discussione della Tesi
15 Luglio 2016

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