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Enhancing Predictive Maintenance Strategies for Industrial Machinery through Hybrid Deep Learning Models and Sensor Fusion

Authors: Anjali Vasishtha

Published: 2025/5/2

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Abstract

This research investigates the application of hybrid deep learning models coupled with sensor fusion techniques to enhance predictive maintenance strategies for industrial machinery. The study addresses the limitations of traditional maintenance approaches and explores the potential of advanced machine learning algorithms to accurately predict equipment failures and optimize maintenance schedules. A novel hybrid model combining Convolutional Neural Networks (CNNs) for feature extraction from time-series sensor data and Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) networks, for temporal dependency modeling is proposed. The model is trained and validated on a comprehensive dataset obtained from various sensors monitoring the operational parameters of industrial equipment. The results demonstrate significant improvements in prediction accuracy, reduced false alarm rates, and optimized maintenance scheduling compared to conventional methods and standalone deep learning models. The proposed approach offers a promising solution for enhancing the reliability and efficiency of industrial operations through proactive maintenance strategies.

Keywords

Predictive Maintenance, Deep Learning, Sensor Fusion, Industrial Machinery, Hybrid Models, Convolutional Neural Networks, Recurrent Neural Networks, Anomaly Detection, Condition Monitoring, Machine Learning

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How to Cite

Anjali Vasishtha, (2025/5/2). Enhancing Predictive Maintenance Strategies for Industrial Machinery through Hybrid Deep Learning Models and Sensor Fusion. JANOLI International Journal of Humanities and Linguistics , Volume UIh3MC5UrwhGKptS6jkQ, Issue 3.

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