Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

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
View PDF6 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • 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

Examples

Experimental program
Comparison scheme
Effect test

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, ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

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

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/044G06N3/045G06F18/2433G06F18/2415Y02P90/30
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
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com