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Electromechanical product assembly error online prediction method

A technology for assembly errors and electromechanical products, applied in prediction, neural learning methods, computer components, etc., to achieve the effects of increasing inference speed, reducing distance, and improving prediction accuracy

Pending Publication Date: 2022-05-24
NANJING UNIV OF SCI & TECH
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  • Claims
  • Application Information

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Problems solved by technology

However, limited by data analysis capabilities and assembly data characteristics, companies have not been able to make good use of this part of the data to establish a mapping relationship between process parameters and assembly errors

Method used

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  • Electromechanical product assembly error online prediction method
  • Electromechanical product assembly error online prediction method
  • Electromechanical product assembly error online prediction method

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Embodiment Construction

[0016] The present invention will be further introduced below with reference to the accompanying drawings and specific embodiments.

[0017] attached figure 1 It is a flow chart of an on-line prediction method of assembly error based on autoencoder and Boosting-OSKELM algorithm. The present invention includes the following steps:

[0018] Step 1. Build a training sample dataset:

[0019] According to the historical data of a certain process, the corresponding data set is constructed, 80% of the data set is divided into training set D, and the remaining 20% ​​is divided into test set E. The difference between the two is only in the amount of sample data, but in the characteristics The structure and output structure are consistent.

[0020] training set X is the process input, Y is the error output, and the training set matrix is ​​as follows:

[0021]

[0022] In the training set matrix, each row is a sample point, N is the number of samples, M 1 with M 2 are the numb...

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Abstract

The invention provides a mechanical and electrical product assembly error online prediction method based on an auto-encoder and a Boosting-OSKELM algorithm, and the method comprises the steps: firstly constructing a corresponding sample data set for a certain assembly process in combination with the assembly technology and assembly error historical assembly data; secondly, carrying out dimensionality reduction on the input process data of each sample point based on an auto-encoder with a Fine-Tuning skill, and forming new order reduction representation data by the process data after dimensionality reduction and original assembly error data; thirdly, inputting the process data subjected to dimension reduction into a Boosting-KELM model, and determining a lifting sequence of a kernel function and a corresponding hyper-parameter in combination with prediction performance on a test set; and finally, according to an incremental learning recursion formula of the online sequential kernel extreme learning machine, forming a Boosting-OSKELM model so as to realize online prediction of the assembly error. According to the method, the assembly error of the mechanical and electrical product can be predicted online.

Description

technical field [0001] The invention belongs to the field of assembly quality of electromechanical products, in particular to an online prediction method for assembly errors of electromechanical products based on an autoencoder and a Boosting-OSKELM algorithm. Background technique [0002] Intelligent manufacturing is an important direction of mechanical and electrical product manufacturing, and is of great significance to promoting the development of machinery, information, electronics and other industries. There are various types and functions of mechanical and electrical products, which have been widely used in civil life, equipment production, marine exploration, aerospace and other fields, and play an important role in ensuring people's livelihood and national strategy. [0003] Electromechanical products, especially high-precision equipment electromechanical products, often have a very complex assembly process. The assembly activities are mostly performed by assembly o...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06N3/08G06K9/62
CPCG06Q10/04G06N3/08G06F18/214Y02P90/30
Inventor 童一飞周彤王淼杨开伟
Owner NANJING UNIV OF SCI & TECH
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