Fault Detection and Isolation in Industrial Systems Based on Spectral Analysis Diagnosis

Abstract

The diagnoses in industrial systems represent an important economic objective in process industrial automation area. To guarantee the safety and the continuity in production exploitation and to record the useful events with the feedback experience for the curative maintenance. We propose in this work to examine and illustrate the application ability of the spectral analysis approach, in the area of fault detection and isolation industrial systems. In this work, we use a combined analysis diagram of time-frequency, in order to make this approach exploitable in the proposed supervision strategy with decision making module. The obtained results, show clearly how to guarantee a reliable and sure exploitation in industrial system, thus allowing better performances at the time of its exploitation on the supervision strategy.

Prediction of diesel engine performance, emissions and cylinder pressure obtained using bioethanol-biodiesel-diesel fuel blends through an artificial neural network

“…These layers are composed of input, hidden and output layers and a desired number of processing components is used in each one ( Figure 6). The processing components in the input layer receive the information from the outside world and deliver it to the middle layer (Hafaifa et al, 2013;Singh et al, 2013). In some networks, no information processing occurs in this layer.…”

The Application of Artificial Neural Network in Prediction of the Performance of Spark Ignition Engine Running on Ethanol-Petrol Blends

“…Multilayer perceptions are the most widely used kind of ANNs. Networks with interconnections that do not form any loops are called feedforward [28,43,44]. Fig.…”

Modelling of surge phenomena in a centrifugal compressor: experimental analysis for control

“…The results of the dimensional analysis, including the definition of dimensionless variables, allow for generalised comparisons between machines; such analyses have been widely discussed in the literature (Cunha, de Souza, Barreto, & de Souza, 2009;Galindo, Arnau, Tiseira, & Piqueras, 2010;Hafaifa, Guemana, & Daoudi, 2013a;Halimi, Hafaifa, Bouali, & Guemana, 2014;Utamura, Fukuda, & Aritomi, 2012). We simply retain the following dimensionless variables to describe the operating point: m √ RT t1 /P t1 D 2 , ND/ √ γ RT t1 , N D 2 P t1 /μRT t1 and γ .…”

“…Several works have estimated the risk for the onset of surge and the consequences for the operation of the machine with more or less empirical analytical models (Galindo, Serrano, Climent, & Tiseira, 2008;Gravdahl, Egeland, & Vatland, 2002;Hafaifa, Djeddi, & Daoudi, 2013;Jiang, Khan, & Dougal, 2006;Pinarbasi, 2008;van Helvoirt & de Jager, 2007). However, the nature of this instability, that is, the basic mechanisms that are responsible, was not well identified.…”