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TECHNICAL PAPERS: Gas Turbines: Controls, Diagnostics, and Instrumentation

Neural Network and Fuzzy Logic Diagnostics of 1x Faults in Rotating Machinery

[+] Author and Article Information
A. El-Shafei, T. A. F. Hassan, A. K. Soliman, Y. Zeyada

 RITEC, New Maadi, Cairo 11435, Egypt

N. Rieger

 STI Technologies, Inc., Rochester, NY 14623

J. Eng. Gas Turbines Power 129(3), 703-710 (Feb 01, 2006) (8 pages) doi:10.1115/1.2227417 History: Received October 01, 2005; Revised February 01, 2006

In this paper, the application of neural networks and fuzzy logic to the diagnosis of faults in rotating machinery is investigated. The learning-vector-quantization (LVQ) neural network is applied in series and in parallel to a fuzzy inference engine, to diagnose 1x faults. The faults investigated are unbalance, misalignment, and structural looseness. The method is applied to a test rig (Hassan, 2003, ASME Paper No. GT 2003-38450), and the effectiveness of the integrated Neural Network and Fuzzy Logic method is illustrated.

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Copyright © 2007 by American Society of Mechanical Engineers
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Figures

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Figure 1

LVQ neural network

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Figure 2

Feed forward network

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Figure 3

Flow chart of measurement procedure

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Figure 4

Fault implementation on test rig

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Figure 5

Training of LVQ network

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Figure 6

Training of feed forward network

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Figure 7

The features of a membership function (20)

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Figure 8

Diagnosis procedure

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Figure 9

Fuzzy logic system

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Figure 10

Misalignment sample amplitude spectrum

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