A novel fault diagnosis technique based on model and computational intelligence applied to vehicle active suspension systems

Author:
, , ,
Year
: 2018
Abstract: This paper introduces a novel approach to design fault detection and diagnosis

systems by using computational intelligence techniques for industrial

plants and processes. According to the increasing development of machine

learning algorithms, selecting an appropriate algorithm for identifying and

estimating the size of faults in dynamic systems can turn in to a challenge.

A notable point in this regard is that although the available intelligent algorithms

have considerable advantages and strengths, they are also vitiated by

some weaknesses too. Therefore, using a proper and intelligent structure

through combining common algorithms can be considered as a new and

appropriate strategy to increase the efficiency and accuracy of fault detection

and diagnosis systems. Given the importance of improving safety and reliability

in vehicles, the proposed approach based on dynamic behavior is implemented

on a simulated nonlinear active suspension system model. The

results obtained from multiple tests indicate that the proposed method with

a novel architecture has managed to increase the accuracy and reduce the

announcement of false alarm and computational load, compared with other

conventional methods. This achievement leads to improvement of diagnostic

systems' performance.
Keyword(s): active suspension systems,computational intelligence,fault detection and diagnosis,KNN,neurofuzzy

network
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