
Computer Prediction Model for Equipment Maintenance Using Cloud Computing and Secure Data-sharing
Author(s) -
Kun Pan,
Yuchen Jiang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2083/4/042042
Subject(s) - cloud computing , predictive maintenance , computer science , field (mathematics) , big data , automation , feature selection , upload , data sharing , feature (linguistics) , the internet , data science , data mining , artificial intelligence , engineering , reliability engineering , world wide web , operating system , mechanical engineering , medicine , linguistics , philosophy , alternative medicine , mathematics , pathology , pure mathematics
A With the popularization of automation in the industrial field, productivity has been greatly improved. However, manufacturing corporations are facing a data tsunami which brings new challenges to predictive maintenance (PdM). In recent years, many approaches and architecture for predictive maintenance have been proposed to solve some of these problems to varying degrees. This paper introduces a general framework based on the Internet of Things, cloud computing and big data analytics for PdM of industrial equipment. In this framework, smart sensors are installed on the device to obtain electrical data, which is then encrypted and uploaded to the cloud platform to predict the health condition by deep learning methods. Several working instances including feature selection, feature fusion, and Remaining Useful Life (RUL) prediction are provided. The effectiveness of the proposed methods is demonstrated by real-world cases.