Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Spring Semester
Year 2019 259934 Integration Technology for Networking Manufacturing Systems 1 3.0 3 ★ HUANG JUNG-TANG 4ME4A
4ME4B
4ME4C
4ME3A
4ME3B
4ME3C 32 6 機械大三、四合開

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2019	259934	Integration Technology for Networking Manufacturing Systems	1	3.0	3	★	HUANG JUNG-TANG	4ME4A 4ME4B 4ME4C 4ME3A 4ME3B 4ME3C	32	6	機械大三、四合開

Course Plan：

Instructor HUANG JUNG-TANG

Email jthuang@ntut.edu.tw

Last Updated 2019-02-18 13:22:53

Course Objective 製造聯網整合技術配合感測器、控制器、網路系統等，讓人、機器人、原物料、零組件、重要物品或設備，取得人、機、料、法、環的訊息，透過大數據分析與人工智慧，執行各種事前診斷預防、維護、事發處置及事後重建與改善的動作。本課程著重於製造聯網整合技術與實施方法，特別在智能工廠技術包含IoT、感測器設計與選擇、機電整合、數據分析、預防診斷與維修、智慧適應控制等。 This course provides a practical guide that lets you discover the technologies and the use cases for the industrial IoT. Taking you through the implementation of industrial processes, specialized control devices, and protocols, it covers the process of identification and connection of different industrial data sources gathered from different sensors. You will be able to connect these sensors, such as AWS IoT, Azure IoT, OEM IoT platforms, and Google IoT, to the cloud network, and extract this data from the cloud itself to your devices. Over time, you will gain the knowledge to obtain the hands-on experience necessary for using open source Node-RED, Kafka, Cassandra, and Python. You will develop streaming and batch-based machine learning algorithms. By the end of this book, you will have mastered the features of Industry 4.0 and will be able to build strong, faster, and more reliable IoT infrastructure within your industry.

Course Schedule Week 1 Introduction to Industrial IoT Week 2 Understanding the Industrial Process and Devices Week 3 Industrial Data Flow and Devices Week 4 Implementing the Industrial IoT Data Flow Week 5 Applying Cybersecurity Week 6 Performing an Exercise Based on Industrial Protocols and Standards Week 7 Developing Industrial IoT and Architecture Week 8 Implementing a Custom Industrial IoT Platform Week 9 Midterm Week 10 Understanding Industrial OEM Platforms Week 11 Implementing a Cloud Industrial IoT Solution with AWS Week 12 Implementing a Cloud Industrial IoT Solution with Google Cloud Week 13 Performing a Practical Industrial IoT Solution with Azure Week 14 Understanding Diagnostics, Maintenance, and Predictive Analytics Week 15 Implementing a Digital Twin – Advanced Analytics Week 16 Deploying Analytics on an IoT Platform Week 17 Deploying Analytics on an IoT Platform Week 18 Final project (oral report)

Evaluation and grading policy Participation 10 % Midterm Exam 40% Final Project 50%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Authors: Giacomo Veneri, Antonio Capasso Hands-On Industrial Internet of Things Copyright c 2018 Packt Publishing Some technical papers may be offered for reference

The access to curricular consultation

Note
　

Instructor	HUANG JUNG-TANG
Email	jthuang@ntut.edu.tw
Last Updated	2019-02-18 13:22:53
Course Objective	製造聯網整合技術配合感測器、控制器、網路系統等，讓人、機器人、原物料、零組件、重要物品或設備，取得人、機、料、法、環的訊息，透過大數據分析與人工智慧，執行各種事前診斷預防、維護、事發處置及事後重建與改善的動作。本課程著重於製造聯網整合技術與實施方法，特別在智能工廠技術包含IoT、感測器設計與選擇、機電整合、數據分析、預防診斷與維修、智慧適應控制等。 This course provides a practical guide that lets you discover the technologies and the use cases for the industrial IoT. Taking you through the implementation of industrial processes, specialized control devices, and protocols, it covers the process of identification and connection of different industrial data sources gathered from different sensors. You will be able to connect these sensors, such as AWS IoT, Azure IoT, OEM IoT platforms, and Google IoT, to the cloud network, and extract this data from the cloud itself to your devices. Over time, you will gain the knowledge to obtain the hands-on experience necessary for using open source Node-RED, Kafka, Cassandra, and Python. You will develop streaming and batch-based machine learning algorithms. By the end of this book, you will have mastered the features of Industry 4.0 and will be able to build strong, faster, and more reliable IoT infrastructure within your industry.
Course Schedule	Week 1 Introduction to Industrial IoT Week 2 Understanding the Industrial Process and Devices Week 3 Industrial Data Flow and Devices Week 4 Implementing the Industrial IoT Data Flow Week 5 Applying Cybersecurity Week 6 Performing an Exercise Based on Industrial Protocols and Standards Week 7 Developing Industrial IoT and Architecture Week 8 Implementing a Custom Industrial IoT Platform Week 9 Midterm Week 10 Understanding Industrial OEM Platforms Week 11 Implementing a Cloud Industrial IoT Solution with AWS Week 12 Implementing a Cloud Industrial IoT Solution with Google Cloud Week 13 Performing a Practical Industrial IoT Solution with Azure Week 14 Understanding Diagnostics, Maintenance, and Predictive Analytics Week 15 Implementing a Digital Twin – Advanced Analytics Week 16 Deploying Analytics on an IoT Platform Week 17 Deploying Analytics on an IoT Platform Week 18 Final project (oral report)
Evaluation and grading policy	Participation 10 % Midterm Exam 40% Final Project 50%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Authors: Giacomo Veneri, Antonio Capasso Hands-On Industrial Internet of Things Copyright c 2018 Packt Publishing Some technical papers may be offered for reference
The access to curricular consultation
Note