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Enhanced Predictive Maintenance Framework for Industrial Machinery using Hybrid Deep Learning and Vibration Signal Analysis

Authors: Pramod Kumar Arya

Published: 2025-05-01 23:33:40.257

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Abstract

This paper presents an enhanced predictive maintenance framework for industrial machinery based on hybrid deep learning techniques and vibration signal analysis. The framework integrates Convolutional Neural Networks (CNNs) for feature extraction from raw vibration data and Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) networks, for temporal dependency modeling and prediction of future machine health. The methodology incorporates data preprocessing, feature engineering, model training, and validation using real-world vibration datasets collected from industrial equipment. The proposed framework demonstrates superior performance in early fault detection and remaining useful life (RUL) prediction compared to traditional machine learning and individual deep learning models. The results highlight the effectiveness of the hybrid approach in improving maintenance scheduling, reducing downtime, and enhancing the overall operational efficiency of industrial systems.

Keywords

Predictive Maintenance, Deep Learning, Vibration Analysis, Convolutional Neural Networks, Recurrent Neural Networks, Industrial Machinery, Machine Learning, Fault Diagnosis, Anomaly Detection, Condition Monitoring

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How to Cite

Pramod Kumar Arya, (2025-05-01 23:33:40.257). Enhanced Predictive Maintenance Framework for Industrial Machinery using Hybrid Deep Learning and Vibration Signal Analysis. JANOLI International Journal of Applied Engineering and Management, Volume UIh3MC5UrwhGKptS6jkQ, Issue 2.

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