Abstract
In order to correctly analyze aeroengine whole-body vibration signals, Wavelet Correlation Feature Scale Entropy (WCFSE) and Fuzzy Support Vector Machine (FSVM) (WCFSE-FSVM) method was proposed by fusing the advantages of the WCFSE method and the FSVM method. The wavelet coefficients were known to be located in high Signal-to-Noise Ratio (S/N or SNR) scales and were obtained by the Wavelet Transform Correlation Filter Method (WTCFM). This method was applied to address the whole-body vibration signals. The WCFSE method was derived from the integration of the information entropy theory and WTCFM, and was applied to extract the WCFSE values of the vibration signals. Among the WCFSE values, theWFSE1andWCFSE2values on the scale 1 and 2 from the high band of vibration signal were believed to acceptably reflect the vibration feature and were selected to construct the eigenvectors of vibration signals as fault samples to establish the WCFSE-FSVM model. This model was applied to aeroengine whole-body vibration fault diagnosis. Through the diagnoses of four vibration fault modes and the comparison of the analysis results by four methods (SVM, FSVM, WESE-SVM, WCFSE-FSVM), it is shown that the WCFSE-FSVM method is characterized by higher learning ability, higher generalization ability and higher anti-noise ability than other methods in aeroengine whole-vibration fault analysis. Meanwhile, this present study provides a useful insight for the vibration fault diagnosis of complex machinery besides an aeroengine.
Highlights
With the increase of the jet thrust, rotating speed, structural dynamic strength and vibration load of aeroengine, more and more vibration faults occur during the operating phases
To improve the precision of aeroengine whole-body vibration fault diagnosis, the Wavelet Corrective Feature Scale Entropy and Fuzzy Support Vector Machine (WCFSE-FSVM) method is proposed to extract the fault features based on the Wavelet Correlation Feature Scale Entropy (WCFSE) method and to build the fault diagnosis model based on the FSVM method
The objective of the effort is proposed the WCFSE-FSVM method to address the samples with noise, exceptional and weak signals for aeroengine whole-body vibration fault diagnosis
Summary
With the increase of the jet thrust, rotating speed, structural dynamic strength and vibration load of aeroengine, more and more vibration faults occur during the operating phases. The feature extraction is a big bottleneck and directly affects the accuracy and validity of aeroengine wholebody vibration fault diagnosis because of the existence of nonlinear and unstable weak fault signals with noise or outliers. The wavelet entropy theory proposed in [2,6] based on wavelet analysis technique is demonstrated to be effective in processing vibration signals in general industrial equipment, directly employing the wavelet entropy method in aeroengine whole-body vibration fault diagnosis is unreasonable since the fault signals with unclear information and low SNR are always disturbed by noise and outlier. To improve the precision of aeroengine whole-body vibration fault diagnosis, the Wavelet Corrective Feature Scale Entropy and Fuzzy Support Vector Machine (WCFSE-FSVM) method is proposed to extract the fault features based on the WCFSE method and to build the fault diagnosis model based on the FSVM method
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