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所在平台: Udemy |
课程主页: https://www.udemy.com/course/statistical-techniques-for-monitoring-industrial-processes/
课程评论：没有评论
课程名称：监测工业过程的统计技术 课程概述：欢迎参加“监测工业过程的统计技术”课程。在此课程中，您将学习主流的单变量和多变量统计技术，这些技术已被证明在复杂流程工厂的健康监测中非常有用。您将通过与过程工业相关的数据集将所学概念付诸实践。现代工业工厂非常复杂，因此，工厂监测是一项必不可少的活动。可以毫不夸张地说，24小时不间断监测过程性能和设备健康状态，以及预测潜在故障已不再是“可有可无”的选择，而是绝对必要的！本课程将为您提供开发过程监测解决方案所需的工具，包括故障检测（过程或信号是否异常）和故障诊断（哪些变量表现异常）两个组成部分。 学习统计过程监控（SPM）的原因：尽管人工神经网络和深度学习目前备受瞩目，但经典统计方法仍然是工业过程监测的基石，并且享有极大的声誉。与神经网络模型相比，诸如主成分分析（PCA）和偏最小二乘法（PLS）等多变量统计技术更易于理解、解释性更强，而且开发和维护更简单；许多成功案例证明，它们的表现往往与复杂模型相当，甚至更优。 您将学到什么：本课程将为您提供关于开发工业级统计过程监控解决方案的一步一步指导，重点强调概念理解和实践实施。具体来说，您将学习：单变量SPM（监测单一过程变量）和多变量SPM（监测相互作用的多个变量）。除了涵盖概念和实施细节，您还将进行多个案例研究，应用所学技术于工业规模系统。您将使用来自实际或模拟搅拌槽反应器、催化裂化单元、炉子、化工厂和聚合反应器的数据。 课程成果：掌握这些技术后，您将能够满足大多数工业过程的监测需求。 先决条件：本课程不要求之前具有Python编程经验。第二部分将提供快速的Python编程和开发环境介绍。此外，无需先前的机器学习经验。
Welcome to your course on Statistical Techniques for Monitoring Industrial Processes where you will learn about the mainstream univariate and multivariate statistical techniques that have proven useful over the years for health monitoring of complex process plants. You will put the concepts learnt into practice using process industry-relevant datasets. Modern industrial plants are complex and therefore, it is a no-brainer that plant monitoring is an essential activity. Without exaggeration, it can be said that 24X7 monitoring of process performance and plant equipment health status, and forecast of impending failures are no longer a ‘nice to have' but an absolute necessity! This course will equip you with the tools necessary to develop process monitoring solutions that includes both the fault detection (is the process or a signal behaving abnormally?) and fault diagnosis (which variables are behaving abnormally) components.Why study SPM (statistical process monitoring)?While artificial neural networks and deep learning grab most of the limelight now-a-days, classical statistical approaches are still are the bedrock of industrial process monitoring and enjoy immense popularity. Compared to neural network models, multivariate statistical techniques like PCA (principal component analysis) and PLS (partial least squares) are simpler to understand, more interpretable, and easier to develop and maintain; several successful stories. and give you equal if not better performance than very complex models.What will you learn?In this course, you will get step-by-step guidance for developing industrial level solutions for statistical process monitoring. Emphasis is placed on conceptual understanding and practical implementations. Specifically, you will: learn about univariate SPM where you want to monitor a single process variable and multivariate SPM where you want to monitor multiple variables that interact with each otherin addition to covering the conceptual and implementation details, you will undertake several case-studies where you employ the learnt techniques on industrial-scale systems. You will work with data obtained from actual and/or simulated stirred tank reactors, catalytic cracking units, furnaces, chemical plants, polymer reactorsOutcome of the courseOnce you have mastered these techniques, you will be able to handle the monitoring needs of majority of the industrial processes. PrerequisitesWe will not assume any prior Python programming experience. Section 2 of the the course provides a quick introduction to Python programming and the development environment. Also, no prior machine learning experience is required.
