Smart Vibration Monitoring Using Advanced Sensor Technique with Display Interface


EOI: 10.11242/viva-tech.01.09.31

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Citation

Suneet Mehta,Sanket Pawar,Aakash Thakur,"Smart Vibration Monitoring Using Advanced Sensor Technique with Display Interface", VIVA-IJRI Volume 1, Issue 9, Article 31, pp. 1-5, 2026. Published by Mechanical Engineering Department, VIVA Institute of Technology, Virar, India.

Abstract

The reliable operation of industrial machinery is essential for maintaining productivity, safety, and cost efficiency in modern manufacturing systems. Mechanical faults such as imbalance, misalignment, bearing wear, and gear damage often remain undetected during early stages, leading to unexpected failures, downtime, and economic losses. Conventional fault diagnosis techniques rely heavily on periodic inspections and expensive proprietary diagnostic tools, which limit scalability and real-time monitoring capability. Recent advancements in artificial intelligence (AI), machine learning (ML), and sensor technologies have enabled intelligent condition-monitoring systems capable of continuous and automated fault detection. Among various non-destructive techniques, vibration-based monitoring has proven highly effective in diagnosing faults in rotating machinery due to its sensitivity to mechanical abnormalities. This paper presents a Smart Vibration Monitoring System using advanced vibration sensors, Fast Fourier Transform (FFT)-based signal processing, and a Random Forest machine learning classifier for fault identification. The system captures real-time vibration signals, extracts meaningful frequency-domain features, and classifies common machinery faults with high accuracy. A real-time display interface visualizes machine health and diagnostic results, enabling predictive maintenance and informed decision-making. The proposed solution offers a low-cost, scalable, and efficient alternative for intelligent fault diagnosis in industrial environments.

Keywords

Vibration Monitoring, Fault Diagnosis, FFT, Machine Learning, Predictive Maintenance, Random Forest

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