Product quality detection method based on deep neural network

A deep neural network and product quality technology, applied in the field of product quality inspection based on deep neural network, can solve problems such as costing a lot of manpower, material resources, time, and damage, and achieve the effects of simplifying calculation steps, improving detection efficiency, and easy convergence

Pending Publication Date: 2020-10-09
海克斯康制造智能技术(青岛)有限公司
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AI Technical Summary

Problems solved by technology

Manual random inspection and equipment measurement require a lot of manpower, material resources and time. For some products that need to check the internal quality of a closed space, manual random inspection means destroying the product

Method used

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  • Product quality detection method based on deep neural network
  • Product quality detection method based on deep neural network
  • Product quality detection method based on deep neural network

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Embodiment 1

[0034] A method for product quality detection based on a deep neural network, comprising the following steps:

[0035] S1. Collect sensor data and perform normalization processing to obtain a data matrix:

[0036] First, install the required sensors on the equipment, such as temperature, humidity, vibration, noise, current, voltage, etc. The installed sensors are non-invasive and will not affect the normal production of the product; during the production period, each product produced is numbered , collect sensor data during the production process of this product, and form sensor feature information corresponding to each product. Normalize the data of each sensor of the product. Due to the dimension of each sensor, different sensors use their own dimension for normalization. The formula is as follows:

[0037]

[0038] in It is the real-time data collected by the sensor, with are the maximum and minimum detectable values ​​of this sensor, respectively. For example, ...

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Abstract

The invention discloses a product quality detection method based on a deep neural network. The method comprises the following steps: S1, acquiring data of a sensor and performing normalization processing to obtain a data matrix; S2, converting the data matrix in the step S1 into M * N * H three-dimensional space data, wherein the H dimension comprises sensor data and three artificial feature vectors; S3, sequentially convolution layers and connection layers of the three-dimensional space data in the step S2 to obtain two-dimensional data; and S4, inputting the two-dimensional data in the stepS3 into a softmax layer to obtain a vector S, and judging the product quality according to the vector S. According to the invention, the effect similar to a recurrent neural network can be achieved intime association, and convergence is easier than that of the recurrent neural network such as LSTM.

Description

technical field [0001] The invention relates to the field of data collection and analysis, in particular to a product quality detection method based on a deep neural network. Background technique [0002] With the vigorous development of global industrialization and artificial intelligence technology, artificial intelligence technology is more and more applied in the field of industrial production. The new industrial Internet development plans of various countries also use artificial intelligence technology as a key promotion technology. At present, in terms of quality control in the field of industrial production, it mainly relies on manual spot checks of products and the use of measuring equipment for measurement. Manual spot checks and equipment measurements take a lot of manpower, material resources and time. For some products that need to check the internal quality of a closed space, manual spot checks mean destroying the product. [0003] Prior art CN109555566A discl...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/044G06N3/045G06F18/2433G06F18/2415
Inventor 李尚勇谢德威王雪涛惠伟
Owner 海克斯康制造智能技术(青岛)有限公司
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