Advanced Vibration Analysis in Smart Factories

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.

Advanced Vibration Analysis, Industrial Internet of Things (IIOT), Industry 5.0 & 4.0, Artificial Intelligence, Predictive maintenance (PDM), Smart Manufacturing, Monitoring system, Signal Processing, Smart Sensor, Digital Twin Technology

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