Predictive Maintenance and System Optimization Using Edge-Integrated AI and Machine Learning Models
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V5I4P102Keywords:
Predictive Maintenance, Edge Computing, Artificial Intelligence, Machine Learning, Industrial IoT, System Optimization, Federated Learning, LSTM, CNNAbstract
In the contemporary industrial setting, predictive maintenance (PdM) has become a ground-breaking approach, where data-driven smartness is used as an advanced method to anticipate equipment failure before it happens. As the number of Industrial Internet of Things (IIoT) devices grows, data acquisition has lost its significance and started to focus on effective and real-time analysis at the network edges. The paper designs a powerful framework of edge-integrated Ml and AI models of predictive maintenance and system optimization. As opposed to the traditional cloud-based models which experience latency, bandwidth performance, and data security issues, the edge-integrated paradigm can be used to process data locally, perform faster data anomaly detection, and render control feedback. The suggested model is a combination of the deep learning technology and federated edge inference, reducing the amount of transmitted data and maintaining high accuracy. The hybrid Edge-Cloud Collaborative Framework (ECCF) is developed that includes both convolutional neural networks (CNNs) and long short-term memory (LSTM) models of sensor data processing. Dynamically, the system optimizes the performance parameters, which include energy consumption, the intensity of vibration, and thermal load positively on the machines. Benchmark analysis using experimental performance on benchmark datasets (NASA C-MAPSS, PHM08) shows that it can predict faults 25-40 percent more accurately with 30 percent shorter decision latency than traditional cloud-centric methods. The present research offers a detailed roadmap of integrating AI-based predictive analytics and edge computing against intelligent, self-optimizing industrial systems, opening the path to Industry 5.0 and integration of cyber-physical intelligence and computing
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