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Equipment fault three-level bidirectional early warning method and system based on edge computing

A device failure and edge computing technology, which is applied in computing, neural learning methods, and faulty computer hardware detection, can solve data security and privacy that cannot be effectively guaranteed, network bandwidth increases, and the underlying request response time is long and other issues to achieve the effects of strong computing power, reduced communication costs, and reduced delays

Active Publication Date: 2020-09-11
杭州雪沉科技信息有限公司
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AI Technical Summary

Problems solved by technology

[0003] Nowadays, IoT devices are widely used in device fault detection. With the increase of sensor nodes, the linear growth of centralized cloud computing capabilities can no longer meet the explosive data growth, and only relying on centralized cloud computing will cause underlying request response time Long-term, network bandwidth increases, and data security and privacy cannot be effectively guaranteed

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  • Equipment fault three-level bidirectional early warning method and system based on edge computing
  • Equipment fault three-level bidirectional early warning method and system based on edge computing
  • Equipment fault three-level bidirectional early warning method and system based on edge computing

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Embodiment Construction

[0112] Such as figure 1 As shown, according to an embodiment of the present invention, a flowchart of a three-level two-way early warning of equipment failure based on edge computing, the method includes the following steps:

[0113] S101: Construct a first-level prediction model based on adaptive exponential smoothing in the acquisition node and the edge node;

[0114] The adaptive exponential smoothing prediction model specifically includes:

[0115] In the collection node, after the sensor obtains real-time data, it performs exponential smoothing prediction. If the predicted value matches the obtained data value, the sample data is taken according to the reduced sampling frequency, and the normal data of length t is uploaded at intervals T As the prediction basis of the edge node, if the predicted value does not match the obtained data value, the sampling frequency is increased, and the data with a high sampling frequency and a length of s including abnormal information is...

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Abstract

The invention provides an equipment fault three-level bidirectional early warning method and system based on edge computing, and the method comprises the steps: building a first-level bidirectional data sensing prediction model based on an adaptive exponential smoothing algorithm, predicting the data of a collection node, carrying out preliminary screening of a fault signal, uploading the fault signal, and reducing the cost of normal signal transmission; a second-stage bidirectional data perception prediction model of an autoregressive moving average algorithm based on extended Kalman filtering is constructed, and is used for further confirming the accuracy of a fault signal, reducing the false alarm rate and reducing the communication cost between a side end and a cloud end; creating a third-stage bidirectional data perception prediction model based on LSTM and BP neural network combination so that strong computing power is achieved based on edge equipment, he accuracy of data is enhanced, underlying requirements are timely responded, thus reducing time delay of cloud layer transmission. According to the invention, bandwidth and time delay consumed in a data acoustic wave communication transmission process are greatly reduced, and early warning is effectively carried out on a fault signal.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence, and in particular relates to a three-level two-way early warning method and system for equipment failure based on edge computing. Background technique [0002] As equipment runs longer online, it often ages and fails. When the equipment is abnormal or faulty, if it cannot be detected in time and dealt with effectively, it will have a great negative impact on the safe and stable operation of the equipment. Therefore, it is of great significance to safe production to carry out fault warning on online equipment; [0003] Nowadays, IoT devices are widely used in device fault detection. With the increase of sensor nodes, the linear growth of centralized cloud computing capabilities can no longer meet the explosive data growth, and only relying on centralized cloud computing will cause underlying request response time The security and privacy of data cannot be effectively guaranteed a...

Claims

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Application Information

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IPC IPC(8): G06F11/22G06F11/30G06N3/04G06N3/08
CPCG06F11/2263G06F11/3072G06N3/049G06N3/084G06N3/044G06N3/045
Inventor 蔡绍滨王宇昊张妍
Owner 杭州雪沉科技信息有限公司
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