Intelligent Multi-Sensor Fusion Framework for Real-Time Industrial Fault Prediction and Energy Optimization

Industrial machinery failures and energy inefficiencies represent critical challenges in modern manufacturing environments, with unplanned downtime costing manufactures between 50, 000 to 250, 000 per hour depending on production scale and industry sector. Traditional reactive maintenance approaches and scheduled preventive maintenance programs fail to detect early-stage equipment degradation patterns, resulting in catastrophic failures, extended production interruptions, and substantial financial losses. This paper presents an intelligent multi-sensor fusion framework for real-time industrial fault prediction and energy optimization utilizing internet of Things (IOT) technology, advanced signal processing techniques, and machine learning algorithms. The proposed system integrates an ESP32 microcontroller as the edge computing device with multiple sensor modalities including MPU6050 three-axis MEMS accelerometer for vibration analysis, ACS712 Hall-effect current sensor for electrical parameter monitoring, resistive voltage divider for power consumption tracking, and DS18B20 digit temperature sensor for thermal condition assessment. The ESP32 continuously samples sensor data at 500 Hz frequency and transmits multi-parameter information via-WIFI WebSocket protocol to a python-based computational server. The server performs Fast Fourier Transform (FFT) analysis on time-domain vibration signals to extract frequency-domain spectra in the 30-500 Hz industrial machinery range, revealing characteristic fault signatures associated with bearing defects, rotor imbalance, shaft misalignment, and mechanical looseness. Multi sensor data fusion combines vibration frequence patterns with electrical parameter anomalies and thermal deviations to improve fault classification accuracy by 40-60% compared to single-parameters monitoring systems. A professional web-based dashboard provides oscilloscope-level visualization with real-time time-domain waveforms, frequency spectrum charts, electrical parameter displays, and thermal monitoring capabilities.