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Predictive Maintenance Optimization for Industrial Machinery using Hybrid Deep Learning and Vibration Analysis

Authors: Krishan kumar Yadav, Dr. Dalia Mohamed Younis

Published: 2025-05-01 16:34:09.439

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Abstract

This research investigates the optimization of predictive maintenance strategies for industrial machinery by leveraging a hybrid approach integrating deep learning techniques with traditional vibration analysis. The study addresses the critical need for minimizing downtime and maintenance costs in industrial settings by developing a predictive model that accurately forecasts potential equipment failures. We propose a hybrid model that combines Convolutional Neural Networks (CNNs) for feature extraction from raw vibration data with Long Short-Term Memory (LSTM) networks for time-series analysis and failure prediction. The model is trained and validated using a comprehensive dataset of vibration signals collected from various industrial machines under different operating conditions. The results demonstrate that the proposed hybrid approach outperforms traditional methods in terms of prediction accuracy, lead time, and overall maintenance cost reduction. The findings highlight the potential of deep learning-enhanced vibration analysis for proactive maintenance scheduling and improved operational efficiency in industrial environments.

Keywords

Predictive Maintenance, Deep Learning, Vibration Analysis, Industrial Machinery, Condition Monitoring, Machine Learning, Hybrid Model, Fault Diagnosis, Anomaly Detection, Time-Series Analysis

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How to Cite

Krishan kumar Yadav, Dr. Dalia Mohamed Younis, (2025-05-01 16:34:09.439). Predictive Maintenance Optimization for Industrial Machinery using Hybrid Deep Learning and Vibration Analysis. JANOLI International Journal of Applied Engineering and Management, Volume UIh3MC5UrwhGKptS6jkQ, Issue 2.

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