Developing an Integrated IoT Cloud Based Predictive Conservation Model for Asset Management in Industry 4.0

作     者：Karnam Shanmugam Kachhti Satyam Thimma Reddy Sreenivasula Reddy 

作者机构：the Department of Computer ApplicationsAnnamacharya Institute of Technology and SciencesKarakambadiTirupathi 517520India the Department of Computer Science and EngineeringAnnamacharya Institute of Technology and SciencesKarakambadiTirupathi 517520India 

出 版 物：《Journal of Social Computing》 (社会计算（英文）)

年 卷 期：2023年第4卷第2期

页      面：139-149页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Industry 4.0 predictive maintenance golden search optimization multilayer hybrid convolution neural network data cleaning 

摘      要：With the advent of Industry 4.0(I4.0),predictive maintenance(PdM)methods have been widely adopted by businesses to deal with the condition of their *** the help of I4.0,digital transformation,information techniques,computerised control,and communication networks,large amounts of data on operational and process conditions can be collected from multiple pieces of equipment and used to make an automated fault detection and diagnosis,all with the goal of reducing unscheduled maintenance,improving component utilisation,and lengthening the lifespan of the *** this paper,we use smart approaches to create a PdM planning *** five key steps of the created approach are as follows:(1)cleaning the data,(2)normalising the data,(3)selecting the best features,(4)making a decision about the prediction network,and(5)producing a *** the outset,PdM-related data undergo data cleaning and normalisation to get everything in order and within some kind of *** next step is to execute optimal feature selection in order to eliminate unnecessary *** research presents the golden search optimization(GSO)algorithm,a powerful population-based optimization technique for efficient feature *** first phase of GSO is to produce a set of possible solutions or objects at *** objects will then interact with one another using a straightforward mathematical model to find the best feasible *** to the wide range over which the prediction values fall,machine learning and deep learning confront challenges in providing reliable *** is why we recommend a multilayer hybrid convolution neural network(MLH-CNN).While conceptually similar to VGGNet,this approach uses fewer parameters while maintaining or improving classification correctness by adjusting the amount of network modules and *** projected perfect is evaluated on two datasets to show that it can accurately predict the future state of components for upkeep prepara