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相关概念视频

Introduction to Statistical Process Control01:15

Introduction to Statistical Process Control

67
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...
67
The X̄ Chart00:58

The X̄ Chart

99
The  x̄ chart is a statistical tool for monitoring the means in a process.
The x̄ chart, often known as the individual control chart, is a crucial tool in statistical process control. It is designed to monitor process behavior and performance over time and is widely used in various industries to ensure that processes are operating at their optimum capacity and within specified limits.
A x̄ chart is constructed by plotting individual measurements of a quality...
99
The R Chart01:02

The R Chart

54
In statistical process control, control charts, particularly R charts, are instrumental in monitoring process variations and identifying non-random patterns that run charts might miss. R charts track the variability within process subgroups, which is crucial when standard deviation use is impractical or unknown process variations exist.
R charts are pivotal for pinpointing shifts in process variability. Stability is indicated when all data points remain within the defined upper and lower...
54
Interpreting R Charts01:22

Interpreting R Charts

49
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
49
Interpreting Run Charts01:25

Interpreting Run Charts

53
Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
53

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相关实验视频

Updated: May 27, 2025

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
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基于滑窗方法和SECNN-BiLSTM的可变长度控制图的模式识别研究.

Tao Zan1, Xiaolong Jia2,3, Xiaoyu Guo1

  • 1Beijing Key Laboratory of Advanced Manufacturing Technology, College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing, 100124, China.

Scientific reports
|February 18, 2025
PubMed
概括

本研究引入了一种新的方法,用于识别使用滑动窗和SECNN-BiLSTM深度学习的可变长度控制图表. 开发的系统准确地识别了控制图的模式,改善了制造业的统计过程控制.

关键词:
云计算是一种云计算.模式识别 模式识别 模式识别这就是SECNN-BiLSTM.滑动窗的方法 滑动窗的方法可变长度的控制图表.

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科学领域:

  • 工业工程 工业工程 工业工程
  • 计算机科学 计算机科学
  • 数据科学数据科学数据科学

背景情况:

  • 统计过程控制 (SPC) 依赖于控制图表来监控生产过程.
  • 现有的控制图表识别方法与可变长度数据作斗争,限制了它们的工业应用.
  • 精确识别可变长度控制图表对于实时过程监控至关重要.

研究的目的:

  • 提出一种用于识别可变长度控制图的新方法.
  • 为实时控制图表识别开发云端集成系统.
  • 提高工业环境中控制图表模式识别的准确性和效率.

主要方法:

  • 一种可变长度控制图表识别方法,将滑窗方法与SE-attention CNN和Bi-LSTM (SECNN-BiLSTM) 网络相结合.
  • 用一个移动窗口方法将单维,变长的控制图数据转换为二维矩阵.
  • 开发一个云端集成系统,利用无线数字校准器,嵌入式设备和云计算.

主要成果:

  • 拟议的SECNN-BiLSTM方法证明了可变长度控制图的有效和准确识别.
  • 模拟和工程应用验证了开发的云端识别系统的有效性.
  • 该方法成功地解决了现有的SPC工具中固定长度数据识别的局限性.

结论:

  • SECNN-BiLSTM 方法为可变长度控制图表识别提供了有效的解决方案.
  • 云端集成系统可在工业环境中实现实用,实时的应用.
  • 这项工作为统计过程控制中更先进的模式识别技术奠定了基础.