IoT-Based Vibration Condition Monitoring System for Rotating Machinery Fault Detection

Abstract

Machinery reliability is a critical factor in industrial operations, as unexpected equipment failures can lead to costly downtime, production delays, and potential safety hazards. This paper presents the development of an Internet of Things (IoT) based vibration condition monitoring system for fault detection in rotating machinery. The system integrates accelerometer sensors with LabVIEW data acquisition software and the Things Board IoT platform to enable real-time monitoring and analysis. Experiments were conducted using a custom test rig that simulated three common fault conditions with varying severity levels. Quantitative results show distinctive vibration patterns of bearing contamination increased y-axis Vrms from 0.99 mm/s for clean conditions to 10.42 mm/s for 6 g contamination. While for mass unbalance increased motor vibration from 0.67 mm/s for no mass to 3.51 mm/s for 30 g of mass and shaft misalignment elevated bearing vibrations from 6.91 mm/s at align conditions to 30.21 mm/s at 6 mm misalignment. The system successfully classified fault conditions based on ISO 10816 Class II vibration thresholds, with 95% detection accuracy for faults exceeding established severity levels. This IoT-integrated approach enhances predictive maintenance capabilities by providing early fault warnings, reducing unplanned downtime, and improving overall machinery reliability in industrial applications.