Research on Condenser Deterioration Evolution Trend Based on ANP-EWM Fusion Health Degree

作     者：Hong Qian Haixin Wang Guangji Wang Qingyun Yan 

作者机构：College of Automation EngineeringShanghai University of Electric PowerShanghai200090China Shanghai Key Laboratory of Power Station Automation TechnologyShanghai200072China Empower Information Technology(Shanghai)Co.Ltd.Shanghai201508China China Nuclear Power Engineering Design Co.Ltd.Shenzhen518052China 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2024年第139卷第4期

页      面：679-698页

核心收录：

学科分类：080706[工学-化工过程机械] 08[工学] 0807[工学-动力工程及工程热物理] 

基　　金：supported by the National Natural Science Foundation of China (51906133) 

主　　题：Condenser health degree improved Mahalanobis distance GC-Informer model Markov error correction 

摘      要：This study presents a proposed method for assessing the condition and predicting the future status of condensers operating in seawater over an extended *** aim is to address the problems of scaling and corrosion,which lead to increased loss of cold *** method involves utilising a set of multivariate feature parameters associated with the condenser as input for evaluation and trend *** methodology offers a precise means of determining the optimal timing for condenser cleaning,with the ultimate goal of improving its overall *** proposed approach involves the integration of the analytic network process with subjective expert experience and the entropy weightmethod with objective big data analysis to develop a fusion health *** mathematical model is constructed quantitatively using the improved Mahalanobis ***,a comprehensive prediction model is developed by integrating the improved Informer model and Markov error *** model takes into account the health status of the equipment and several influencing factors,includingmultivariate feature *** model facilitates the objective examination and prediction of the progression of equipment deterioration *** present study involves the computation and verification of the field time series data,which serves to demonstrate the accuracy of the condenser health-related models proposed in this *** models effectively depict the real condition and temporal variations of the equipment,thus offering a valuable method for determining the precise cleaning time required for the condenser.