Intelligent Integrated Model for Improving Performance in Power Plants

作     者：Ahmed Ali Ajmi Noor Shakir Mahmood Khairur Rijal Jamaludin Hayati Habibah Abdul Talib Shamsul Sarip Hazilah Mad Kaidi 

作者机构：Razak Faculty of Technology and InformaticsUTM54100Kuala LumpurMalaysia University of TechnologyMinistry of Electricity GCEP/Middle Region BaghdadIraq 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第70卷第3期

页      面：5783-5801页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported/funded by the Ministry of Higher Education/University of Technology Malaysia under the Fundamental Research Grant Scheme(FRGS/1/2019/TK08/UTM/02/4) 

主　　题：Industry 4.0 artificial intelligence critical success factors decision making integrated management system maintenance 

摘      要：Industry 4.0 is expected to play a crucial role in improving energy management and personnel performance in power *** performance problem in maintaining power plants is the result of both human errors,human factors and the poor implementation of automation in energy *** problem can potentially be solved using artificial intelligence(AI)and an integrated management system(IMS).This article investigates the current challenges to improving personnel and energy management performance in power plants,identifies the critical success factors(CSFs)for an integrated intelligent framework,and develops an intelligent framework that enables power plants to improve *** theoretical basis is founded on a systematic literature review to locate 110 out of 3108 papers studied carefully to examine the performance architecture that best enables effective *** findings from this literature review are combined with expert judgment and the big data advantages of AI applications to develop an intelligent *** are collected from a power plant in *** ensure the reliability of the proposed model,various hypotheses are tested using structural equation *** results confirm that the measurement model is acceptable,and that the hypotheses are supported and significant.A case study demonstrates the strong relationship and significance between big data of performance and the *** is hoped that this model will be adopted to enable performance improvement in power plants.