Research on Vertical SEC Centrifugal Pump Multi-Fault Diagnosis Based on WPT–SVM

Abstract

To diagnose common failures in vertical Essential Service Water Pumps (SEC), a method combining the wavelet packet transform (WPT) and the support vector machine (SVM) was adopted. This allowed us to construct a diagnostic model capable of classifying multiple states, including the six types of faults and normal conditions in SEC pumps. The diagnostic model utilized the wavelet packet coefficients to capture sub-bands with a higher energy share and reconstruct the signals. The model inputs the 12 frequency features into the support vector machine to analyze the vibration signals gathered from the SEC pump benchmark. The study illustrates that the proposed method can accurately differentiate between various fault conditions when compared to the WPT method, combined with the artificial neural network (ANN) approach. It attains a superior overall precision of up to 94%, and it displays excellent generalization and strong adaptability.

Keywords

• SEC pump
• vertical centrifugal pump
• fault diagnosis
• wavelet packet decomposition
• frequency domain features
• support vector machine