Framework and development of fault detection classification using IoT device and cloud environment

By Hyunsoo Lee. 2017

91 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Industrial and Manufacturing Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Ala Al-Fuqaha
2. Angelos Angelopoulos
3. Mohsen Guizani
4. Sameh Sorour
5. T. Zahariadis
6. Panagiotis Trakadas
7. Khaled Hamed Salah
8. Nikolaos Nomikos

In Industrial and Manufacturing Engineering

1. Ala Al-Fuqaha
2. Mohsen Guizani
3. Sameh Sorour
4. M. Mohammadi

Citations in the corpus

• Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “In another study reported in [121], a DBN model was proposed in conjunction with an IoT deployment and cloud platform to support fault detection of defect types in cars’ headlight modules in a vehicle manufacturer setting.”
• From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where (2022, unknown): “Lee [30] presented a computer vision and DBN-DL based fault detection and classification system for the manufacturing industry.”
• The Role of Big Data Analytics in Industrial Internet of Things (2019, background): “Large-scale distributed cyber manufacturing systems are based on multiple interconnected but geographically dispersed manufacturing units [86].”
• Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects (2019, background): “, 2017 [34] Improvement of process quality and dynamic mixed data flow control SVR, RBF, and DBL-DL”