An equipment health state assessment and prediction method based on industrial big data

A health status and prediction method technology, applied in data processing applications, electrical digital data processing, digital data information retrieval, etc., to achieve the effect of scientific health status

Pending Publication Date: 2019-06-14
TONGJI UNIV
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  • An equipment health state assessment and prediction method based on industrial big data
  • An equipment health state assessment and prediction method based on industrial big data
  • An equipment health state assessment and prediction method based on industrial big data

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[0060] Example

[0061] The method for evaluating and predicting the health status of equipment based on industrial big data described in this example is as follows: figure 1 with image 3 , Mainly includes the following parts:

[0062] S1. First select several sets of relatively complete run-to-failure full life cycle data from the equipment state data set, and select characteristic parameters that can characterize the equipment degradation state and can be continuously monitored and recorded as equipment degradation variables. Different types of industrial equipment can choose their own parameters to be monitored, mainly including speed, flow rate, pressure, temperature, power, current, etc.

[0063] S2. Perform effective data preprocessing for data-related degraded variables, including normalization and feature reduction based on principal component analysis, and eliminate redundant variables in sample data;

[0064] Among them, the normalization adopts the maximum and minimum nor...

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Abstract

The invention relates to an equipment health state assessment and prediction method based on industrial big data. The method comprises the following steps of S1, extracting sample data capable of representing the whole degradation process of equipment from an equipment state monitoring system; S2, performing effective preprocessing on the sample data; S3, constructing a distributed support vectordata description model based on the Spark platform, and extracting a normal sample set; S4, measuring the deviation degree of the current sample through the Euclidean distance, further converting thedeviation degree into a health degree value, and drawing a health degree curve; S5, constructing an equipment health degree single-step prediction model based on the Spark platform; and S6, further expanding the single-step prediction method of the equipment into multi-step prediction. Compared with the prior art, the invention relates to the equipment health assessment and prediction method, objectively and accurately assesses the health state of the monitored equipment, predicts the future degradation trend of the equipment, and provides a theoretical basis for the fault management and maintenance work of the subsequent equipment.

Description

technical field [0001] The invention relates to the technical field of equipment health management, in particular to a method for evaluating and predicting equipment health status based on industrial big data. Background technique [0002] The performance of equipment will slowly decline with the increase of service time. Effectively evaluating and predicting the health status of equipment is of great significance for preventing failures and improving equipment reliability. Equipment health status evaluation refers to the health degree to describe the overall operation of the equipment, which is a comprehensive evaluation of the equipment operation status. Equipment health status prediction refers to mining the internal evolution law of equipment health to realize the advanced prediction of equipment health, which is convenient for equipment maintenance and management. [0003] The equipment health assessment method commonly used in the industrial field is the health assess...

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Application Information

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IPC IPC(8): G06Q10/00G06Q10/06G06F16/215G06F16/2458G06K9/62
CPCY02P90/30
Inventor 乔非张连连翟晓东
Owner TONGJI UNIV
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