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- W2971396385 endingPage "101981" @default.
- W2971396385 startingPage "101981" @default.
- W2971396385 abstract "Using multi-source sensing data based on the Internet of Things (IoT) with artificial intelligence and big data processing technology to achieve predictive maintenance of mechanical equipment can remarkably improve the service life of the machine and reduce labor costs when diagnosing mechanical faults, and it has become a highly relevant research topic. In this paper, the multi-source sensing data fusion models and fusion algorithms are studied and discussed. First, the Joint Directors of Laboratories (JDL) fusion model and the Hierarchical fusion model are compared and analyzed. Then, various types of fusion algorithms based on Neural Networks and Deep Learning, including Dempster-Shafer (D-S) evidence theory and their applications in mechanical fault diagnosis and fault prediction, are studied and compared. The findings reveal that exploring and designing a more intelligent fusion model incorporating the beneficial characteristics of different fusion algorithms are challenging and have a certain value for promoting the development of mechanical fault diagnosis and prediction." @default.
- W2971396385 created "2019-09-12" @default.
- W2971396385 creator A5003356618 @default.
- W2971396385 creator A5008188376 @default.
- W2971396385 creator A5076579785 @default.
- W2971396385 date "2020-07-01" @default.
- W2971396385 modified "2023-10-17" @default.
- W2971396385 title "Mechanical fault diagnosis and prediction in IoT based on multi-source sensing data fusion" @default.
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