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相关概念视频

Quality Control01:05

Quality Control

169
Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
169
Introduction to Statistical Process Control01:15

Introduction to Statistical Process Control

126
Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...
126
Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

107
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
107
Quality Assurance01:19

Quality Assurance

129
Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
129
Interpreting X̄ Charts01:13

Interpreting X̄ Charts

67
Interpreting x̄ charts, a type of control chart used in statistical process control helps monitor the variation in processes over time. The x̄ chart is based on the sample mean and allows for monitoring variations in the process mean over time. These charts are pivotal for quality assurance in manufacturing and other sectors.
An x̄ chart plots the values of individual measurements over time against control limits calculated from historical data. The central line...
67

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相关实验视频

Updated: Jul 5, 2025

Operation of the Collaborative Composite Manufacturing CCM System
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基于边缘计算的产业产品制造质量预测的积极控制方法.

Mo Chen1, Zhe Wei2,3, Li Li1

  • 1School of Mechanical Engineering, Shenyang University of Technology, Shenyang, China.

Scientific reports
|January 13, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的SMOTE-XGboost方法,用于准确预测产品质量,特别是在智能制造中的不平衡数据. 边缘计算提高了效率,在制动盘生产中表现优于传统模型.

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科学领域:

  • 制造业 工程 制造工程
  • 人工智能的人工智能
  • 数据科学数据科学数据科学

背景情况:

  • 智能制造集成了诸如大数据和人工智能等先进技术,用于增强工业流程.
  • 产品质量预测是一个关键的应用,但传统模型在制造业中常见的不平衡数据集中扎.
  • 现有的云计算模型在工业环境中面临带宽和资源限制.

研究的目的:

  • 建议使用不平衡数据进行产品质量预测的有效方法.
  • 解决工业制造环境中传统云计算的局限性.
  • 在现实生产线上验证拟议方法的实用性和有效性.

主要方法:

  • 一种合成少数人过量采样技术 (SMOTE) 与XGBoost (SMOTE-XGboost) 结合,用于不平衡的数据分类.
  • 对SMOTE-XGboost模型的超参数进行联合优化.
  • 结合边缘计算技术来克服云计算的局限性.

主要成果:

  • 拟议的SMOTE-XGboost方法在预测车盘质量方面表现优异,与其他分类方法相比.
  • 边缘计算有效地解决了工业应用中的带宽和资源限制.
  • 该方法的实用性和有效性通过一个案例研究来验证.

结论:

  • 在产品质量预测中,SMOTE-XGboost方法为不平衡的数据分类提供了强大的解决方案.
  • 边缘计算集成增强了智能制造中先进预测模型的可行性.
  • 这种方法显著提高了在制动盘生产等工业环境中的质量预测准确度.