Open AccessComparison of deep learning models for predictive maintenance
Author(s) -
R. Naren,
J. Subhashini
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/912/2/022029
Subject(s) - computer science , complex event processing , intersection (aeronautics) , license , event (particle physics) , predictive analytics , internet of things , artificial intelligence , analytics , machine learning , real time computing , data science , computer security , engineering , physics , process (computing) , quantum mechanics , aerospace engineering , operating system
There is a clear intersection between the Internet of Things (IoT) and Artificial Intelligence (AI). IoT is about connecting machines and making use of the data generated from those machines. AI is about simulating intelligent behaviour in machines of all kinds. As IoT devices will generate vast amounts of data, then AI will be functionally necessary to deal with these huge volumes if we’re to have any chance of making sense of the data. AI is beneficial for both real-time and post event processing: Post event processing – identifying patterns in data sets and running predictive analytics, e.g. the correlation between traffic congestion, air pollution and chronic respiratory illnesses within a city centre. Real-time processing – responding quickly to conditions and building up knowledge of decisions about those events, e.g. remote video camera reading license plates for parking payments.