Affiliation:
1. National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China
2. China Nuclear Power Engineering Corporation Limited, Beijing 100840, China
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.
Funder
Natural Science Foundation of China
Subject
Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction
Reference18 articles.
1. Zhao, J.P., and Yan, G.H. (2010). Advances in Hydraulic Physical Modeling and Field Investment and Investigatio, China Waterpower Press-Cwpp6 Sanlihelu, Fuxingmenwai.
2. Analysis and research on vibration characteristics of nuclear centrifugal pumps at low flow rates;Luo;Energy Rep.,2022
3. Zhao, X.L., Yao, J.Y., Deng, W.X., Ding, P., Ding, Y.F., Jia, M.P., and Liu, Z. (2022). IEEE Transactions on Neural Networks and Learning Systems, IEEE.
4. A flexible algorithm for fault diagnosis in a centrifugal pump with corrupted data and noise based on ANN and support vector machine with hyper-parameters optimization;Azadeh;Appl. Soft Comput. J.,2013
5. Multi-fault diagnosis for rotating machinery based on orthogonal supervised linear local tangent space alignment and least square support vector machine;Su;Neurocomputing,2015