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.
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.
This paper investigates the optimization of Hybrid Renewable Energy Systems (HRES) for rural electrification, addressing the critical need for sustainable and affordable energy access in remote areas. We propose a novel approach integrating Multi-Criteria Decision-Making (MCDM) techniques with an enhanced Whale Optimization Algorithm (WOA) to determine the optimal HRES configuration. The objective function considers technical, economic, and environmental factors, including Levelized Cost of Energy (LCOE), Net Present Cost (NPC), renewable energy fraction (REF), and greenhouse gas emissions. A case study is presented for a rural community in India, utilizing HOMER Pro for initial simulation and the enhanced WOA for subsequent optimization. Results demonstrate the superior performance of the proposed method compared to conventional approaches, achieving significant reductions in LCOE and emissions while ensuring reliable power supply. This research contributes to the advancement of sustainable energy solutions for rural communities, fostering economic development and environmental stewardship.
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.
This research investigates the application of hybrid deep learning models for enhancing predictive maintenance strategies in industrial machinery. The approach integrates sensor fusion techniques to leverage data from multiple sensor modalities (vibration, temperature, pressure) and employs anomaly detection algorithms to identify deviations from normal operating conditions. A hybrid model, combining Convolutional Neural Networks (CNNs) for feature extraction and Long Short-Term Memory (LSTM) networks for temporal dependency modeling, is proposed. The model's performance is evaluated on a real-world dataset of industrial pump operations, demonstrating significant improvements in prediction accuracy and reduced false alarm rates compared to traditional methods. The results highlight the potential of the proposed approach for proactive maintenance planning, minimizing downtime, and optimizing operational efficiency.