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Deep Learning Based Intelligent Industrial Fault Diagnosis Model

computers, Materials & continua 2022年第3期70卷 6323-6338页

作者： R.Surendran Osamah Ibrahim Khalaf c.rlos andres tavera romero center for Artificial Intelligence&Research(cAIR) Chennai Institute of TechnologyChennai600069India Al-Nahrain Nanorenewable Energy Research center Al-Nahrain UniversityBaghdad64074Iraq cOMBA I+D Research Group of Universidad Santiago de cali Santiago de CaliColombia

In the present industrial revolution era,the industrial mechanical system becomes incessantly highly intelligent and ***,it is necessary to develop data-driven and monitoring approaches for achieving quick,trustable,and high-quality analysis in an automated *** diagnosis is an essential process to verify the safety and reliability operations of rotating *** advent of deep learning(DL)methods employed to diagnose faults in rotating machinery by extracting a set of feature vectors from the vibration *** paper presents an Intelligent Industrial Fault Diagnosis using Sailfish Optimized Inception with Residual Network(IIFD-SOIR)*** proposed model operates on three major processes namely signal representation,feature extraction,and *** proposed model uses a continuous Wavelet Transform(cWT)is for preprocessed representation of the original vibration *** addition,Inception with ResNet v2 based feature extraction model is applied to generate high-level ***,the parameter tuning of Inception with the ResNet v2 model is carried out using a sailfish ***,a multilayer perceptron(MLP)is applied as a classification technique to diagnose the faults *** experimentation takes place to ensure the outcome of the presented model on the gearbox dataset and a motor bearing *** experimental outcome indicated that the IIFD-SOIR model has reached a higher average accuracy of 99.6%and 99.64%on the applied gearbox dataset and bearing *** simulation outcome ensured that the proposed model has attained maximum performance over the compared methods.

关键词： Intelligent models fault diagnosis industrial control deep learning feature extraction

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Internet of Things Enabled Energy Aware Metaheuristic clustering for Real Time Disaster Management

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computer Systems Science & Engineering 2023年第5期45卷 1561-1576页

作者： Riya Kumarasamy Santhanaraj Surendran Rajendran c.rlos andres tavera romero Sadish Sendil Murugaraj Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India center for Artificial Intelligence and Research(cAIR) Chennai Institute of TechnologyChennai600069India cOMBA R&D Laboratory Faculty of EngineeringUniversidad Santiago de CaliCali76001Colombia Department of Emerging Technologies Guru Nanak Institute of TechnologyIbrahipatnamTelangana501506India

Wireless Sensor Networks(WSNs)are a major element of Internet of Things(IoT)networks which offer seamless sensing and wireless *** management in smart cities can be considered as a safety critical ***,it becomes essential in ensuring network accessibility by improving the lifetime of IoT assisted *** and multihop routing are considered beneficial solutions to accomplish energy efficiency in IoT *** article designs an IoT enabled energy aware metaheuristic clustering with routing protocol for real time disaster management(EAMcR-RTDM).The proposed EAMcR-RTDM technique mainly intends to manage the energy utilization of nodes with the consideration of the features of the disaster *** achieve this,EAMcR-RTDM technique primarily designs a yellow saddle goatfish based clustering(YSGF-c)technique to elect cluster heads(cHs)and organize *** addition,enhanced cockroach swarm optimization(EcSO)based multihop routing(EcSO-MHR)approach was derived for optimal route *** YSGF-c and EcSO-MHR techniques compute fitness functions using different input variables for achieving improved energy efficiency and network *** design of YSGF-c and EcSO-MHR techniques for disaster management in IoT networks shows the novelty of the *** examining the improved outcomes of the EAMcR-RTDM system,a wide range of simulations were performed and the extensive results are assessed in terms of different *** comparative outcomes highlighted the enhanced outcomes of the EAMcRRTDM algorithm over the existing approaches.

关键词： Internet of things disaster management wireless sensor networks real time applications routing clustering

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