Recurrent Neural Networks for Fault Diagnosis and Severity Assessment of a Screw Compressor

[+] Author and Article Information
C. James Li, Yimin Fan

Department of Mechancial Engineering, Aeronautical Engineering and Mechanics, Rensselaer Polytechnic Institute, Troy, NY 12180-3590

J. Dyn. Sys., Meas., Control 121(4), 724-729 (Dec 01, 1999) (6 pages) doi:10.1115/1.2802542 History: Received August 01, 1997; Online December 03, 2007


This paper describes a method to diagnose the most frequent faults of a screw compressor and assess magnitude of these faults by tracking changes in compressor’s dynamics. To determine the condition of the compressor, a feedforward neural network model is first employed to identify the dynamics of the compressor. A recurrent neural network is then used to classify the model into one of the three conditions including baseline, gaterotor wear and excessive friction. Finally, another recurrent neural network estimates the magnitude of a fault from the model. The method’s ability to generalize was evaluated. Experimental validation of the method was also performed. The results show significant improvement over the previous method which used only feedforward neural networks.

Copyright © 1999 by The American Society of Mechanical Engineers
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