Industrial monitoring increases the reliability of plants and processes, including gas turbines. Turbine vibrations can degrade the unit's working condition and be symptoms of future failure. Their continuous operating state places a premium on quick and early detection of problems. This article discusses the use of a neural network to examine vibrations of a gas turbine GE MS3002 at the Hassi R'mel gas plant in southern Algeria, which moves residual dry gas into the Algerian pipeline network for an eventual shipment to Europe via a sub-Mediterranean pipeline and represents the architecture modeling of the examined gas turbine system after modeling of its data system. The network can learn vibrations in an operating gas turbine and use them to help design future networks used to plan preventive maintenance. Modeling neural networks to vibration patterns of operating gas turbines and then applying these models to the same turbines' future performance and maintenance can reduce costs and improve operations. The turbine assembly consists of three main elements: axial compressor, combustor, and turbine. The first step in diagnosing the turbine is to choose a neural model; the second step is to generate residuals, and the third to decide a course of action to address localized defects. The choice of the neural model in learning to identify the turbine's dynamic behavior depends strictly on the complexity of the state, comparing healthy functioning with malfunctioning, used to generate a neural model. This study modeled the turbine's behavior, using a "feed forward" network type and a multilayer perception (MLP) model. It presents - among other things - equations for network parameters such as for the input and output vector, for calculating the outputs of neurons of the hidden and the output layer, for the linear and sigmoid activation functions, and describes the two phases model behavior of the artificial neuron. The learning algorithm by the back-propagation gradient error, or any short back-propagation algorithm, provides the basis for adjusting the physical process's weight vector to minimize the defined quadratic cost function.
Vibration modeling improves pipeline performance, costs
Oil and Gas Journal ; 113 , 3(+ Pos.) ; 98-100
2015
3 Seiten, Bilder, Tabellen, 2 Quellen
Article (Journal)
English
Algorithmus , Backpropagation , dynamisches Verhalten , Gasturbine , Gewichtungsfunktion , Instandhaltung , Kostenfunktion , Lernalgorithmus , Lernprozess , Modell , Modellierung , Multilayer-Perzeptron , Netzwerkdesign , neuronales Netzwerk , quadratische Funktion , Schwingungsüberwachung , Überwachung , Vibration , vorbeugende Instandhaltung
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