Neuro-Fuzzy Methods for Fault Diagnosis of Nonlinear Systems

Mehennaoui, Lamine; Debbache, Nasreddine; Benloucif, M. L.

doi:10.3923/jas.2006.2020.2030

Cited by 3 publications

(1 citation statement)

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“…Research on these topics has been discussed in this section. Mehennaoui et al, [21] reported a fuzzy neural scheme for sensor fault diagnosis in nonlinear systems. It focuses on fuzzy logic as a local identification method and neural network as a global identification method to detect sensor faults in a three-tank process.…”

Section: Introductionmentioning

confidence: 99%

Is Fault Detection and Diagnosis in Pneumatic Actuator A Topic of Concern?

Navada

Santhosh

2020

ARFMTS

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The present civilization highly depends on industrial products and hence there is an increased demand for the same. Therefore, each industry is trying to increase its production output without hindering the quality. Maintenance of plant health is essential to improve the production rate without any loss. Industrial processes require monitoring of every element as their consistent behavior is a fundamental concern. Any deviation in the working of these components may alter the quality of the end product, causing a huge loss for the industry. Therefore, monitoring and finding the root cause for irregular behavior of industrial processes is a requisite for avoiding any future loss. In this paper, an attempt is made to present types of faults, types of pneumatic actuator faults, and different techniques used for the detection and isolation of faults. Simulation work is carried out to generate stiction behavior in the control valve using the Choudhury stiction model. Valve stiction behavior for different values of stick band and jump values are discussed in this paper. A comparison of several techniques used for the detection of faults based on two performance indices namely true detection rate and false alarm rate has been given at the end of this paper. From these techniques, it is observed that these indices are interdependent, such that an increase in the detection rate increases the false detection rate and increases detection time.

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Section: Introductionmentioning

confidence: 99%