Journal of Applied Science and Engineering

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Baofan Chen1, Chunrong Zhou2, and Zhenghong Jiang2This email address is being protected from spambots. You need JavaScript enabled to view it.

1College of Intelligent ManufacturingChongqing Water Resources And Electric Engineering College, Yongchuan 402160 ,Chongqing China

2School of Big Data, Chongqing Vocational College of Transportation, Jiangjin 402247, Chongqing, China


Received: May 3, 2023
Accepted: March 4, 2024
Publication Date: May 23, 2024

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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This research presents a new method for identifying faults in gas turbine compressors, using vibration analysis and statistical tests inside the support vector machine (SVM) algorithm. In the proposed technique, the dynamic signals are first received in the frequency domain, and the investigated frequency domain is divided into smaller ranges. Then, the RMS of each range is extracted as a frequency feature and given as input to the SVM algorithm. Because a large selection of features causes the classification accuracy to decrease, and also to select better features, the extracted feature vector is first passed through T-test filters with different significance levels and then given as input to the SVM algorithm. This method, while increasing the classification accuracy from 80.9% to 99.4%, helps the recognition of frequency ranges, which have noticeable variations under the influence of the fault. Based on the obtained results, compressor faults mostly increase the intensity of vibrations in frequency bands above 1500 Hz.

Keywords: Fault diagnosis, gas turbine compressors, vibration analysis, support vector machine.

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