Abstract
An intelligent fault diagnosis model of identical machines with different operating conditions has been developed earlier. The model is based on the experimental vibration data with several rotor related faults in the experimental rig and their faults diagnoses through artificial neural network (ANN). This method is further validated through the finite element model of the experimental rig by simulating the different rotor faults. The concept needs now to be integrated together to realise a centralised vibration-based condition monitoring (CVCM) system by putting all identical machines in a pool. The CVCM system can then perform the data collection from machines, data storage and data processing leading to the machine diagnosis. The design concept of the CVCM system is proposed in this paper mainly using artificial intelligence (AI), cloud computing, machine identifier using the global positioning system (GPS) location and Industry 4.0 internet of things (IIoT). The paper also highlights the requirements and challenges to meet and implement the proposed CVCM in practice.
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Acknowledgments
This study was supported by CONICYT (Comisión Nacional de Investigación Científica y Tecnológica/Chilean National Commission for Scientific and Technological Research) “Becas Chile” Doctorate’s Fellowship programme; Grant No. 72190062 to Natalia Fernanda Espinoza Sepúlveda.
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Espinoza-Sepulveda, N.F., Sinha, J.K. (2022). Design for Vibration-Based Fault Diagnosis Model by Integrating AI and IIoT. In: Karim, R., Ahmadi, A., Soleimanmeigouni, I., Kour, R., Rao, R. (eds) International Congress and Workshop on Industrial AI 2021. IAI 2021. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-93639-6_23
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DOI: https://doi.org/10.1007/978-3-030-93639-6_23
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