The Prediction of Faults Using Large Amounts of Industrial Data

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An essential feature of an intelligent workplace is error-free manufacturing. The data-driven approaches rely on fundamental status data with minimal volume, while the conventional model-based methods rely heavily on accurate equipment models. Not only are these aspects problematic, but they also prevent them from meeting the real-time need of evaluating industrial big data in an IoT setting. In this research, we provide a fault prediction approach that uses industrial big data to unearth the connection between data (such as status and sound data) and equipment failures using machine learning techniques. Not only that but the breakdown could be investigated promptly since the equipment's status could be tracked in real-time. Our solution outperforms the current ones in terms of accuracy and real-time capabilities, according to the simulation results.