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A neural network-based method for gas turbine blading fault diagnosis

Stavrakakis Georgios, Pouliezos, A.D., 1951-, E.N. Loukis, C. Angelakis

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Summary

In this paper artificial neural networks are used with promising results in a critical, and at the same time, very difficult problem concerning the diagnosis of gas turbine blading faults. Neural network-based fault diagnosis is treated as a pattern recognition problem, based on measurements and feature selection. Emphasis is given to the design of the appropriate neural network architecture and the selection of the appropriate measuring instruments, which are of critical importance for achieving good performance (high success rates and generalization capabilities). Initially the performance of the classical neural network architectures, namely MultiLayer Perceptron (MLP), Learning Vector Quantization (LVQ), Modular MultiLayer Perceptron and Radial Basis Function (RBF), are investigated for this problem. The implemented neural network structures are trained to classify faulty and healthy patterns coming from twelve different measuring instruments. The performance of the above neural network structures is investigated, and the diagnostic capabilities of the measuring instruments are examined. Next, in order to improve the generalization capabilities, which are critical for the specific diagnostic problem, a new multinet architecture is developed, based on the idea of 'majority rule' decision. Compared with the classical architectures, this new multinet architecture is characterized by higher generalization capabilities and robustness. A first approach to the design of the appropriate multinet architecture and the selection of the appropriate measuring instruments, in order to provide the basis of a high-performance automated diagnostic system, is proposed. The conclusions derived are of general interest and applicability.

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