AI-Powered Predictive Maintenance: A Deep Learning Approach for Industrial IoT
DOI:
https://doi.org/10.15662/IJARCST.2021.0406009Keywords:
AI, Predictive Maintenance, Deep Learning, Industrial IoT, CNN-LSTM, Fault Diagnosis, Time-Series Analysis, Anomaly Detection, Edge Computing, Industry 4.0Abstract
The rapid advancement of Artificial Intelligence (AI) and the proliferation of the Internet of Things (IoT) have transformed traditional industrial systems into highly interconnected and intelligent Industrial Internet of Things (IIoT) environments. In this context, predictive maintenance (PdM) has emerged as a crucial application area aimed at reducing unplanned downtimes, optimizing asset utilization, and enhancing operational efficiency. This research paper, titled “AI-Powered Predictive Maintenance: A Deep Learning Approach for Industrial IoT,” presents a comprehensive deep learning-based framework for fault detection, prediction, and maintenance scheduling in industrial systems. By leveraging sensor data from IIoT networks, the proposed model utilizes advanced deep learning architectures—such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Autoencoders—to extract hidden patterns, temporal dependencies, and anomalous behaviors from complex time-series data.
The study begins by highlighting the limitations of traditional preventive and condition-based maintenance approaches, which often rely on fixed schedules or manual feature engineering. These methods fail to adapt dynamically to the changing operational conditions of modern industrial assets. To address these challenges, the proposed AI-driven predictive maintenance framework integrates a data acquisition layer, a deep learning analytics layer, and a decision support layer. The data acquisition layer gathers multi-source sensor data (vibration, temperature, pressure, and acoustic signals) from connected machinery. The analytics layer preprocesses this data using noise reduction and normalization techniques before feeding it into a hybrid CNN-LSTM model. CNNs capture spatial correlations and local features within the sensor data, while LSTMs effectively model the temporal evolution of machine states. The Autoencoder-based anomaly detection module further enhances the system’s ability to identify early signs of degradation.
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