• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Data driven process monitoring based on neural networks and classification trees.
【24h】

Data driven process monitoring based on neural networks and classification trees.

机译：基于神经网络和分类树的数据驱动过程监控。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Process monitoring in the chemical and other process industries has been of great practical importance. Early detection of faults is critical in avoiding product quality deterioration, equipment damage, and personal injury. The goal of this dissertation is to develop process monitoring schemes that can be applied to complex process systems.; Neural networks have been a popular tool for modeling and pattern classification for monitoring of process systems. However, due to the prohibitive computational cost caused by high dimensionality and frequently changing operating conditions in batch processes, their applications have been difficult. The first part of this work tackles this problem by employing a polynomial-based data preprocessing step that greatly reduces the dimensionality of the neural network process model. The process measurements and manipulated variables go through a polynomial regression step and the polynomial coefficients, which are usually of far lower dimensionality than the original data, are used to build a neural network model to produce residuals for fault classification. Case studies show a significant reduction in neural model construction time and sometimes better classification results as well.; The second part of this research investigates classification trees as a promising approach to fault detection and classification. It is found that the underlying principles of classification trees often result in complicated trees even for rather simple problems, and construction time can excessive for high dimensional problems. Fisher Discriminant Analysis (FDA), which features an optimal linear discrimination between different faults and projects original data on to perpendicular scores, is used as a dimensionality reduction tool. Classification trees use the scores to separate observations into different fault classes. A procedure identifies the order of FDA scores that results in a minimum tree cost as the optimal order. Comparisons to other popular multivariate statistical analysis based methods indicate that the new scheme exhibits better performance on a benchmarking problem.
机译：化学和其他过程工业中的过程监视具有非常重要的现实意义。尽早发现故障对于避免产品质量下降，设备损坏和人身伤害至关重要。本文的目的是开发可应用于复杂过程系统的过程监控方案。神经网络已经成为用于监视过程系统的建模和模式分类的流行工具。然而，由于在分批处理中由于高尺寸和频繁变化的操作条件而导致的计算成本过高，因此其应用一直很困难。这项工作的第一部分通过采用基于多项式的数据预处理步骤解决了这个问题，该步骤大大降低了神经网络过程模型的维数。过程测量值和受控变量经过多项式回归步骤，并且通常比原始数据维数低得多的多项式系数用于构建神经网络模型，以产生用于故障分类的残差。案例研究表明，神经模型构建时间显着减少，有时分类结果也更好。本研究的第二部分研究了分类树，将其作为一种有前途的故障检测和分类方法。发现分类树的基本原理即使对于相当简单的问题也常常导致复杂的树，而对于高维问题则构造时间可能过多。 Fisher判别分析（FDA）具有降维功能，它具有不同故障之间的最佳线性判别能力，并将原始数据投影到垂直分数上。分类树使用分数将观测值分为不同的故障类别。一个程序可以识别FDA评分的顺序，从而将最小的树木成本作为最佳顺序。与其他流行的基于多元统计分析的方法的比较表明，新方案在基准测试问题上表现出更好的性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号