Advanced Vibration Analysis in Smart Factories

Susmita Mistri, Souradip Roy and Hao-Chung Kuo
Additional contact information
Susmita Mistri: International Ph.D. Program in Photonics, Institute of Electro-Optical Engineering, College of Electrical and Computer Engineering, National Yang - Ming Chiao Tung University, Hsinchu, 30010 Taiwan, Republic of China.
Souradip Roy: General interface Solution (GIS), Foxconn, No.2, Houke S. Road, Houli District, Taichung City – 42152, Taiwan, Republic of China.
Hao-Chung Kuo: Semiconductor Research Center, Hon Hai Research Institute, Taipei, 11492 Taiwan, Department of Photonics, Institute of Electro-Optical Engineering, National Yang - Ming Chiao Tung University, Hsinchu, 30010 Taiwan, Republic of China.

International Journal of Latest Technology in Engineering, Management & Applied Science, 2025, vol. 14, issue 4, 507-518

Abstract: In the development of smart factories, advanced vibration analysis is essential since it allows for real-time machinery and equipment monitoring and diagnostics. Smart sensor integration signifies continuous collection, processing, and analysis of vibration data to identify early indications of mechanical failures and maintain maximum performance in intricate industrial systems. With a focus on how artificial intelligence (AI) is revolutionizing conventional diagnostic approaches, this chapter examines the most recent developments in vibration analysis techniques. Machine learning and deep learning algorithms are used in AI-driven vibration diagnostics to identify complex defect patterns that traditional techniques could overlook. On top of that, that combine vibration data from several sources improves diagnostic precision and resilience, making it possible to identify problems in big, networked systems. By integrating sensor data with advanced signal processing techniques like wavelet transformation and Fast Fourier Transform (FFT), a complete image of system health is produced, allowing for predictive maintenance and minimizing downtime. This chapter shows how as we go toward Industry 5.0, AI, sensor technology, and vibration data fusion work together to improve smart factory operations, increase overall system reliability, and facilitate the long-term growth of manufacturing sectors.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.ijltemas.in/DigitalLibrary/Vol.14Issue4/507-518.pdf (application/pdf)
https://www.ijltemas.in/papers/volume-14-issue-4/507-518.html (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bjb:journl:v:14:y:2025:i:4:p:507-518

Access Statistics for this article

International Journal of Latest Technology in Engineering, Management & Applied Science is currently edited by Dr. Pawan Verma

More articles in International Journal of Latest Technology in Engineering, Management & Applied Science from International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS)
Bibliographic data for series maintained by Dr. Pawan Verma ().