Design and implementation of CfoTS networks for industrial fault detection and correction mechanism

  • S. KarthikeyanEmail author
  • K. Vimala Devi
  • K. Valarmathi


In the industry, large- and small-scale manufacturers and even original equipment manufacturers are facing a major problem in monitoring large data. Because the amount of data is increasing daily, detecting faults and the methodology of detecting faults are becoming increasingly complex, such that there are insufficient intelligent data-driven mechanisms for achieving a short response time and high accuracy. Intelligent systems utilizing the advantages of Internet of Things (IoT) are emerging; however, they still require innovation. To design an intelligent system for a fault detection system, we propose a new fog-based IoT framework called cognitive Fog of Things framework, for achieving improved industrial fault detection and correction. The proposed framework comprises fog area networks including sensor nodes, fogs, and machine learning algorithms for detection and prediction. The proposed network operates on message queue transportation telemetry and cognitive learning fogs. The proposed concept is developed in a real-time scenario using Raspberry Pi with different case studies for implementation and using various parameters such as different types of faults, time of computation, detection time, and accuracy.


CfoTS FAN IoT Machine learning algorithms MQTT Cognitive learning fogs 



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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electronics and Instrumentation EngineeringM. Kumarasamy College of EngineeringKarurIndia
  2. 2.School of Computer Science and EngineeringVellore Institute of TechnologyVelloreIndia
  3. 3.Department of Electronics and Communication EngineeringP.S.R. Engineering CollegeSivakasiIndia

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