Neural Networks for Gas Turbine Fault Identification: Multilayer Perceptron or Radial Basis Network?

Abstract

Efficiency of gas turbine condition monitoring systems depends on quality of diagnostic analysis at all its stages such as feature extraction (from raw input data), fault detection, fault identification, and prognosis. Fault identification algorithms based on the gas path analysis may be considered as an important and sophisticated component of these systems. These algorithms widely use pattern recognition techniques, mostly different artificial neural networks. In order to choose the best technique, the present paper compares two network types: a multilayer perceptron and a radial basis network. The first network is being commonly applied to recognize gas turbine faults. However, some studies note high recognition capabilities of the second network. For the purpose of the comparison, both networks were included into a special testing procedure that computes for each network the true positive rate that is the probability of a correct diagnosis. Networks were first tuned and then compared using this criterion. Same procedure input data were fed to both networks during the comparison. However, to draw firm conclusions on the networks’ applicability, comparative calculations were repeated with different variations of these data. In particular, two engines that differ in an application and gas path structure were chosen as a test case. By way of summing up comparison results, the conclusion is that the radial basis network is a little more accurate than the perceptron, however the former needs much more available computer memory and computation time.

A Review on Gas Turbine Gas-Path Diagnostics: State-of-the-Art Methods, Challenges and Opportunities

“…In another study, PNNs were used for sensor fault diagnostics in a jet engine for on-board application [151]. Like the other ANNs, PNN applies the concept of pattern recognition technique for fault isolation and identification tasks [152]. It uses a probabilistic measure to decide the type and location of the fault and to assess its magnitude.…”

“…Radial basis function networks (RBF), also called a kernel function, is a multivariate approximation function whose value depends only on the distance from the origin or center c, i.e., the Euclidean distance [152]. It performs a nonlinear transformation over the input vector before it is fed for classification.…”

“…The Euclidean distance is computed from the point being evaluated to the center of each neuron, and an RBF is applied to the distance to compute the weight for each neuron [152]. The further a neuron is from the point being evaluating the less influence it has.…”

Nonlinear Steady-State Model Based Gas Turbine Health Status Estimation Approach with Improved Particle Swarm Optimization Algorithm

“…At large part-load operating range, combustor keeps high combustion efficiency for a clean or healthy combustion chamber. Introducing the combustor health parameter as described in (10), the performance characteristic of actual combustor can be expressed as follows:…”

“…Many gas-path analysis approaches have been proposed to estimate the performance and health status for gas turbine. From the linear GPA method developed by Urban in the late 1960s [2], a series of GPA methods were proposed, such as thermodynamic model based GPA methods (e.g., adaptive linear and nonlinear GPA methods [3][4][5] and genetic algorithm based GPA method [6][7][8][9]) and artificial intelligence based GPA methods (e.g., artificial neural networks [10,11], rule based expert system [12][13][14], and rule based fuzzy expert system [15]).…”