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VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/1874
Title: Anomaly detection in predictive maintenance for internet of things applications
Authors: Tran Huu Phuc 
Keywords: IOT applications;AI model
Issue Date: 2024
Abstract: 
The amalgamation of artificial intelligence (AI) with Internet of Things (IoT) offers substantial prospects for predictive maintenance across diverse applications, empowering systems to self-monitor, evaluate, and react to environmental data instantaneously. This research investigates the creation of intelligent software intende to handle, track, and identify irregularities in Internet of Things (IoT) sensor data by applying a Convolutional Neural Network (CNN) model. The system is structured around four main modules: Data Preparation and Processing, Training, Prediction, and a Mobile App interface. Data from multiple sensors is processed and used to train a CNN model, which then predicts future values and identifies any deviations that could indicate potential issues. The Mobile App displays these insights in a user-friendly manner, providing real-time alerts for any detected anomalies. Despite challenges such as the need for extensive training data and the complexity of developing cross-platform applications, the project demonstrates the potential of combining IoT and AI for predictive maintenance. Future enhancements could further refine the system's accuracy, speed, and user experience, making it a valuable tool in various industrial and domestic settings.
URI(1): https://epub.vgu.edu.vn/handle/dlibvgu/1874
Rights: Attribution-NonCommercial 4.0 International
Appears in Collections:Computer Science (CS)

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