Remote equipment health prediction method based on machine learning and edge computing
A technology of edge computing and remote equipment, applied in the direction of instruments, electrical testing/monitoring, testing/monitoring control systems, etc., can solve problems such as reduced management efficiency, impact on production activities, property loss, etc., to achieve the effect of reducing impact
Inactive Publication Date: 2020-10-02
NANJING INTELLIGENT MFG RES INC
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The invention discloses a remote equipment health prediction method based on machine learning and edge computing, and the method comprises the following steps: deploying an edge computing terminal integrating computing, storage, network and application core capabilities on the edge side close to equipment or a data source, realizing connection with production equipment in combination with a communication module, and acquiring operation element data of the production equipment in real time; providing intelligent data analysis services nearby; employing the intelligent analysis model based on machine learning for completing cleaning and preliminary analysis processing of a large amount of real-time data, triggering possible analysis service response according to a deployed prediction mode strategy, and uploading an analysis result to the cloud; and then completing comprehensive analysis and prediction of the data through the cloud intelligent model. The cloud management architecture realizes efficient management of the unattended equipment terminal, and the distributed attribute of the edge computing effectively reduces the data processing load of the cloud platform and ensures the data security at the same time. Operation health state management and fault prediction of remote equipment are effectively realized.
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