Equipment cluster health state evaluation method based on industrial big data

A technology of health status and equipment status, which is applied in the engineering field to achieve the effect of improving storage and processing efficiency, ensuring real-time and high efficiency

Inactive Publication Date: 2017-11-17
XIDIAN UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem of quantitative evaluation of differences in large-scale cluster equipment. Aiming at the deficiencies of existing technologies, a method for evaluating the health status of equipment clusters based on industrial big data is proposed

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  • Equipment cluster health state evaluation method based on industrial big data
  • Equipment cluster health state evaluation method based on industrial big data
  • Equipment cluster health state evaluation method based on industrial big data

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Embodiment 1

[0029] Traditional analysis methods can only conduct health modeling and analysis for a single working condition and a single device. As the scale of equipment continues to expand, the shortcomings of the efficiency and accuracy of previous analysis and modeling methods are undoubtedly exposed. They do not have the ability to model equipment clusters. This in turn affects the safe and efficient operation of cluster devices.

[0030] In view of the problems existing in the above analysis methods, the present invention conducts research and discussion, introduces cluster modeling technology, and proposes a method for evaluating the health status of equipment clusters based on industrial big data, see figure 1 , the cluster health status assessment method is based on the equipment big data environment generated during the entire service cycle of the equipment cluster, including equipment operating status parameters, equipment operating condition data, environmental parameters duri...

Embodiment 2

[0038] The composition of the method for evaluating the health status of equipment clusters based on industrial big data is the same as in Embodiment 1, refer to image 3 , using the entity state slicing management method to perform data preprocessing on the data, including the following specific steps:

[0039] Step 2.1 Classify the equipment big data according to the degree of correlation with the equipment status level, and determine the reference node for equipment status update according to the data type. The data is mainly divided into two categories: the first category is fault data and maintenance record data, directly As a reference node for equipment status updates. The second type is state parameter data, which needs to use the subsequence extraction method of working condition parameters as the reference node for equipment state update.

[0040] Step 2.2 Cluster equipment data is mainly the second type of data, so the processing method for the second type of data ...

Embodiment 3

[0046] The composition of the method for evaluating the health status of equipment clusters based on industrial big data is the same as in Embodiment 1-2, refer to Figure 4 , for complex multi-working-condition operating data, automatic clustering of operating conditions is performed on the operating parameter data of the equipment. The cluster obtained by clustering according to the similarity of working conditions is used as the smallest unit of the cluster, also called the equipment set. The same modeling method can be used within the equipment set to facilitate unified analysis. The working condition automatic clustering method specifically includes the following steps:

[0047] Step 3.1 Carry out automatic clustering of working conditions on the operating parameter data of the equipment. First, the determination coefficient θ is given, 0C , and randomly take a sample as the first cluster center Z 1 , such as taking Z 1 =x 1 .

[0048] Step 3.2 Find new cluster cente...

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Abstract

The invention discloses an equipment cluster health state evaluation method based on industrial big data and solves a health state evaluation problem of equipment clusters. Based on the full service period big data environment of the equipment clusters, an entity state information slicing management model is constructed to pre-process data; a condition similarity clustering method is utilized to carry out equipment cluster division, a regression method is utilized to establish a mirror image model, the mirror image model and an entity health state are mutually mapped, and a health state quantification model of the different equipment clusters is acquired; fusion and reconstruction of the health state data of the different equipment clusters are carried out to acquire health degrees of the different pieces of equipment, fitting of an equipment health state degeneration curve is carried out, and residual service life of the equipment is predicted. The method is advantaged in that a big data clustering modeling method is utilized, not only can effectively different evaluation on the equipment cluster health state be carried out, but also model redundancy of cluster modeling can be reduced, the internal structure of the model is simplified, normal operation of the cluster equipment is guaranteed, and use values of the equipment can be mined to a maximum degree.

Description

technical field [0001] The invention belongs to the field of engineering technology, and relates to equipment health state modeling and evaluation, in particular to a method for evaluating the health state of equipment clusters based on cluster modeling, which can be used for quantitative evaluation of differences in the health state of equipment clusters. Background technique [0002] Equipment health status assessment mainly refers to the analysis based on the data measured by installed sensors, manual measurement data, historical data, experimental data, etc., comprehensively considering the influence of equipment use, environment, maintenance and other factors, and using various evaluation algorithms to establish models , according to the specified evaluation index system to evaluate the health status of the equipment, a technology to clarify the health status of the equipment. Correctly evaluating the health status of equipment and accurately predicting the remaining li...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06F17/50G06F17/30
CPCG06F16/90G06F30/20G06Q10/0639
Inventor 孔宪光王继虎常建涛刘尧
Owner XIDIAN UNIV
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