The Use of Kalman Filter and Neural Network Methodologies in Gas Turbine Performance Diagnostics: A Comparative Study

Abstract

The goal of gas turbine performance diagnositcs is to accurately detect, isolate, and assess the changes in engine module performance, engine system malfunctions and instrumentation problems from knowledge of measured parameters taken along the engine’s gas path. The method has been applied to a wide variety of commercial and military engines in the three decades since its inception as a diagnostic tool and has enjoyed a reasonable degree of success. During that time many methodologies and implementations of the basic concept have been investigated ranging from the statistically based methods to those employing elements from the field of artificial intelligence. The two most publicized methods involve the use of either Kalman filters or artificial neural networks (ANN) as the primary vehicle for the fault isolation process. The present paper makes a comparison of these two techniques.

Dynamic neural networks for gas turbine engine degradation prediction, health monitoring and prognosis

“…Ogaji in [14] has further extended multiple neural networks method to generate a cascaded network to isolate component and sensor faults. Volponi et al in [29] introduced a hybrid neural network where part of the model was replaced by influence coefficients. They reported that the accuracy of such a network was favorably compared with a backpropagation network and Kalman filter approach.…”

Soft Computation of Turbine Inlet Temperature of Gas Turbine Power Plant Using Type-2 Fuzzy Logic Systems

“…Soft computing techniques like fuzzy logic and neural network has attracted the interest of researchers in the field of gas turbine and power. Recently, few papers have applied soft computing tools to fault detection and isolation, machinery health monitoring, diagnosis and control [7][8][9][10]. Ganguli [7] has suggested fuzzy-logic system for fault isolation in a gas turbine engine.…”

“…Ganguli [7] has suggested fuzzy-logic system for fault isolation in a gas turbine engine. Volponi [8] compares the Kalman filter and ANN (Artificial Neural Network) approaches for fault isolations in a gas turbine. Romesis and Mathioudakis [1] used probabilistic neural networks (PNN) to detect sensor faults on a gas turbine engine.…”

A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant

“…Gas turbines, with special focus on monitoring [38] and prediction [39] of performance, condition monitoring as an alternative to conventional scheduled maintenance [40], operation optimization of turbine compressor [41], health monitoring in military aircrafts engines [42] and turboshaft engines for helicopter propulsion [43], gas turbine diagnostics [44], fault isolation [45] with observation of model accuracy at partial load operation [46] and creep life prediction [47]. Boilers, with developed models aimed at predicting the performance of pulverized-coal boilers [48], predicting NO x emissions of CFB (circulating fluidised bed) boilers [49], estimating pollutant emissions in chain-grate stoker boilers [50], modelling NO x emissions in pulverized-coal boilers [51] with subsequent application of Genetic Algorithm for optimization purposes [52], predicting bottom ash depositing in pulverized-coal power plants [53] and reproducing the influence of fouling on the efficiency of biomass boilers [54].…”