Method of aeroengine fault diagnosis based on a Fingerprint map

Abstract

The electrification and intelligentization trends of aeroengine systems present new challenges for the diagnostics of traditionally centralized control architectures. Scientific and practical fault diagnosis is of great significance for reducing aeroengine failures, improving operation efficiency, and ensuring safety. To realize accurate fault diagnosis, an aeroengine fault diagnosis method based on an improved fingerprint map is proposed in this paper. Based on aeroengine data analysis, ISTOA-SVM diagnostic model that embeds improved fingerprint map data as a priori knowledge is adopted to identify the fault sets. Data based on the statistics of the fingerprint map are processed with a ratio coefficient, and the feature parameters are appended through Floyd-principal component analysis (Floyd-PCA) to increase the identification data dimension. On the other hand, to improve the diagnostic performance of the classifier, the sooty terns optimization algorithm algorithm combined with chaotic maps, a variable helix mechanism and a separating operator is designed, and the ISTOA-SVM is constructed. The fault diagnosis method was validated by using a real-world dataset of aeroengines. Comparison experiments show that the proposed method is superior in performance over the state-of-the-art approaches in diagnostic precision under the constraint of limited data. The ISTOA-SVM addresses the issue that similar features may be confused in traditional fingerprint maps. The results are presented through 5 practical cases. The study demonstrates that the proposed method achieves superior performance and can be applied to aeroengine fault diagnosis.