Volume 36 Issue 10
Oct.  2021
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SUN Hao, FU Xuyun, ZHONG Shisheng. Slow feature density clustering-based gas path anomaly detection method[J]. Journal of Aerospace Power, 2021, 36(10): 2218-2229. doi: 10.13224/j.cnki.jasp.20200382
Citation: SUN Hao, FU Xuyun, ZHONG Shisheng. Slow feature density clustering-based gas path anomaly detection method[J]. Journal of Aerospace Power, 2021, 36(10): 2218-2229. doi: 10.13224/j.cnki.jasp.20200382
shu

Slow feature density clustering-based gas path anomaly detection method

doi: 10.13224/j.cnki.jasp.20200382

  • School of Ocean Engineering，Harbin Institute of Technology，Weihai，Weihai Shandong 264209，China

  • Received Date: 2020-09-11
  • Publish Date: 2021-10-28
    Abstract
  • Based on the kernel method，slow feature analysis algorithm and density clustering method，a mixed-kernel slow feature density clustering algorithm was proposed to detect the original gas path parameters of civil aero-engine.The introduction of the kernel method overcame the possible dimensional explosion when the slow feature analysis dealt with complex data，and it took full use of the characteristics of different kernel functions and advantages of slow feature analysis.This algorism can extract the feature with the slowest time-dependent change from the original gas path parameters and use it as the input of the density clustering algorithm.Then the anomalies were found out.Through experimental validation，this method had the best clustering results and the lowest false alarm rate on certain anomalies.Especially，the number of false alarms was less than 0.5% of the total number of samples when detecting the anomaly of the variable bleed valve system，proving it is an efficient method.

     

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