Research on Blade Fault Diagnosis Model Based on CEEMDAN-SVM
The occurrence of cracks in fan blades,caused by harsh working environments and high-speed operation,has become a hidden danger restricting their performance and safety.To address this issue,this study proposes a comprehensive crack detection model based on vibration signal processing and artificial intelligence techniques.By monitoring the vibration signals of fan blades in real-time,the raw signals are processed using wavelet denoising techniques.Subsequently,crack features are extracted using CEEMDAN and Hilbert-Huang Spectrum Analysis(HSA).Finally,a crack classification model is established using Support Vector Machines(SVM).This research provides a new approach to improving the safety and reliability of mine axial flow fan equipment.
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