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Industrial big data mining-based state predicting method

A prediction method and big data technology, applied in neural learning methods, electrical digital data processing, special data processing applications, etc., can solve problems such as the difficulty of predicting system states, and achieve enhanced adaptability, significant training effects, and low computing costs. Effect

Inactive Publication Date: 2017-06-13
QUANZHOU INST OF EQUIP MFG
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AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a state prediction method based on industrial big data mining to solve the problem that the system state is difficult to predict in the current industrial system and improve the stability and reliability of prediction

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  • Industrial big data mining-based state predicting method

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Embodiment Construction

[0046] Such as figure 1 A state prediction method based on industrial big data mining disclosed in this embodiment includes the following steps:

[0047] Step 1, data collection: use the samples reflecting the historical operation status of the system as the training set where x i is the system state variable, that is, the input of the model, t i is the predictor of interest, the output of the model;

[0048] Step 2, OS-ELM model: use the training samples from step 1 Establish several OS-ELM models and calculate several predicted values;

[0049] Step 3, EOS-ELM model: average the prediction results of the OS-ELM model to obtain the prediction result of the EOS-ELM model.

[0050] The OS-ELM model building process includes:

[0051] Before initialization, first determine the initial parameters of the network: the network has L hidden nodes, and determine the type of hidden nodes;

[0052] In the initialization phase, from the training samples Select some samples f...

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Abstract

The invention discloses an industrial big data mining-based state predicting method. The method comprises the steps of step 1, data acquisition, i.e., taking a sample reflecting a system history operating state as a training set, wherein xi is a system state variate, i.e., the input of a model, and ti is a concerned predictive index, i.e., the output of the model; step 2, building OS-ELM (online sequential extreme learning machine) models, i.e., using a training sample of step 1 to build a plurality of OS-ELM models, and calculating to obtain a plurality of predictive values; step 3, building EOS-ELM (enhanced online sequential extreme learning machine) models, i.e., averaging predicting results of the OS-ELM models to obtain predicting results of the EOS-ELM models. The problem that the system state in the current industrial system is difficultly predicted is solved, and stability and reliability of prediction are improved.

Description

technical field [0001] The invention belongs to the field of industrial big data mining, in particular to a state prediction method based on industrial big data mining Background technique [0002] With the increasing size and complexity of industrial systems, people have higher and higher requirements for the safety and reliability of system operation. The connection between the systems is closer, and the failure of one component will lead to the failure of the subsystem or even the paralysis of the whole system. These problems will bring huge economic losses to the enterprise, and even cause environmental pollution and even casualties. Estimating the operating status of the system in advance and predicting the time and location of abnormalities are effective means to eliminate potential dangers in time, maintain the normal operation of the system, and improve safety and economic benefits. [0003] Neural network, Markov model, Bayesian estimation and ELM are common predic...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00G06N3/08
CPCG06N3/08G16Z99/00
Inventor 陈豪张景欣蔡品隆王耀宗张丹骆炜钟瑞宇
Owner QUANZHOU INST OF EQUIP MFG
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