Hadoop based intelligent sensing system and method for health status of equipment

Abstract

The invention belongs to the field of related technologies of industrial big data processing and discloses a Hadoop based intelligent sensing system and method for the health status of equipment. The intelligent sensing system for the health status of the equipment comprises a plurality of sensors, a Hadoop platform, a data management module, a data processing module, a status monitoring module, a service life monitoring module and a data visualization module, wherein the plurality of sensors are used for detecting status data of the equipment; the Hadoop platform is used for performing distributed processing on the status data; the data management module is used for importing and managing the status data; the data processing module is used for processing the status data and storing the processed state data in a classified manner; the status monitoring module is used for recognizing and predicting the status of the equipment; the service life monitoring module is used for predicting the service life of the equipment; the data visualization module is used for extracting the status data for visualized display.

Description

technical field [0001] The invention belongs to the technical field related to industrial big data processing, and more specifically, relates to a Hadoop-based intelligent perception system and method for equipment health status. Background technique [0002] Driven by emerging technologies such as cloud computing, big data and the Internet of Things, the world has set off a new round of industrial transformation with the transformation and upgrading of manufacturing as the primary task. In this round of industrial transformation, networking, digitalization, and intelligence have become the key directions for the development of the manufacturing equipment industry, and how to timely and accurately diagnose and predict the health status of manufacturing equipment has become one of the key points. Most of the traditional research on the diagnosis and prediction of the health status of manufacturing equipment is based on the status monitoring data collected during the movement ...

AI Technical Summary

Problems solved by technology

However, with the update of detection methods and the recursion of monitoring time, more and more types of data can be obtained, the amount of data is increasing, and the density of data value is getting lower and lower, while the speed of data processing is required to be fast, but the existing The method can no longer meet the needs of processing speed

[0003] As a recent research hotspot at home and abroad, industrial big data analysis technology has been paid more and more attention by more and more units, but the mass condition monitoring data of assembly cannot be processed in a timely and effective manner, resulting in low efficiency and low reliability, which is not conducive to Accurate detection of equipment health status

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