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TBM cutting tool life prediction method based on data driven support vector regression machine

A technology of support vector regression and tool life, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve the problem of no actual progress in the study of dynamic factors, shorten the construction period, improve precision, and avoid difficult expressions Effect

Inactive Publication Date: 2017-05-31
TUNNEL ENG CO LTD OF CHINA RAILWAY 18TH BUREAU GRP
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Problems solved by technology

At present, the research on TBM tool wear at home and abroad is only from the aspects of mechanics and manufacturing materials, and there is no actual progress in the research of dynamic factors.

Method used

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  • TBM cutting tool life prediction method based on data driven support vector regression machine
  • TBM cutting tool life prediction method based on data driven support vector regression machine
  • TBM cutting tool life prediction method based on data driven support vector regression machine

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Embodiment Construction

[0026] The content of the present invention will be described in detail below in conjunction with specific embodiments:

[0027] Data driven:

[0028] Data drive is generated on the basis of big data. It needs to use big data technical means to analyze and process the massive data of enterprises, and dig out the value contained in these massive data, so as to guide enterprises in production, sales, operation, manage.

[0029] The principle of support vector machine regression machine:

[0030] Support vector machine (support vector machine, SVM) was first proposed by Corinna Cortes in 1995, and it has shown many advantages in solving small sample, nonlinear and high-dimensional recognition. The support vector machine method is established on the basis of the VC dimension theory and the minimum structural risk theory in statistical learning theory, based on the complexity of the model (learning accuracy for specific training samples) and learning ability (identifying any The...

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Abstract

The invention relates to the technical field of tunnel excavators, in particular to a TBM cutting tool life prediction method based on a data driven support vector regression machine. The method comprises the following steps that 1, data of the TBM cutting tool excavation site is collected; 2, driving factors that affect the TBM cutting tool life is determined, and a sample data set of the driving factors is established as a training set; 3,a prediction model of a multi-kernel support vector regression machine is constructed, the training set is input, training is conducted on the prediction model to determine corresponding optimal parameters and penalty function C and insensitive loss function parameter epsilon; 4, an optimal kernel function of the prediction model is determined; 5, the sample data set of the driving factors of the cutting tools to be predicted serves as a prediction sample set, the prediction model is input, and a prediction result is obtained. According to the TBM cutting tool life prediction method based on the data driven support vector regression machine, a large amount of data of the excavation site is selected as parameters, and on the basis of a model based on support vectors regression machine is constructed, and the accuracy of a prediction tool is improved.

Description

technical field [0001] The invention relates to the technical field of tunnel boring machines, in particular to a TBM tool life prediction method based on a data-driven support vector regression machine. Background technique [0002] With the rapid development of cities, subway, as an important part of three-dimensional transportation, has become an effective way to solve urban congestion and has great development potential. Urban geological conditions are generally diverse. The full-section tunnel boring machine (Tunnel Boring Machine, TBM) for hard rock tunnel construction is a complete set of advanced tunneling equipment that integrates excavation, support, and slag discharge. It is huge, and changing the tool is time-consuming and labor-intensive, which affects the construction period. Whether the time for tool replacement can be shortened becomes an important factor for the efficient use of TBM. Accurately predicting the wear of each tool can help the TBM construction ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
CPCG16Z99/00
Inventor 施红忠
Owner TUNNEL ENG CO LTD OF CHINA RAILWAY 18TH BUREAU GRP
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