Enhanced Predictive Maintenance for Industrial Machinery using Hybrid Machine Learning and IoT Sensor Fusion

Anjali Vasishtha

Enhanced Predictive Maintenance for Industrial Machinery using Hybrid Machine Learning and IoT Sensor Fusion

Authors: Anjali Vasishtha

Published: 2025-05-01 15:02:31.638

Abstract

This research paper presents an enhanced predictive maintenance (PdM) framework for industrial machinery utilizing a hybrid machine learning approach coupled with IoT sensor fusion. The framework integrates data from multiple sensor modalities (vibration, temperature, pressure, acoustic emissions) to provide a comprehensive assessment of equipment health. A novel hybrid model, combining a deep learning-based autoencoder for feature extraction and a Random Forest classifier for anomaly detection and Remaining Useful Life (RUL) prediction, is proposed. The effectiveness of the proposed framework is validated using a real-world industrial dataset, demonstrating significant improvements in prediction accuracy and reduced false alarm rates compared to traditional methods. The results highlight the potential of this approach for optimizing maintenance schedules, minimizing downtime, and improving the overall efficiency of industrial operations.

Keywords

Predictive Maintenance, Machine Learning, IoT, Sensor Fusion, Condition Monitoring, Anomaly Detection, Industrial Automation, Equipment Health, Remaining Useful Life (RUL)

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How to Cite

Anjali Vasishtha, (2025-05-01 15:02:31.638). Enhanced Predictive Maintenance for Industrial Machinery using Hybrid Machine Learning and IoT Sensor Fusion. JANOLI International Journal of Applied Engineering and Management, Volume UIh3MC5UrwhGKptS6jkQ, Issue 2.

ISSN: 3048-6939

JANOLI International Journal of
Applied Engineering and Management ( JIJAEM )