Industrial AI

Jay Lee, Industrial AI: Applications with Sustainable Performance, Springer, 2020.

This book introduces Industrial AI in multiple dimensions. Industrial AI is a systematic discipline which focuses on developing, validating and deploying various machine learning algorithms for industrial applications with sustainable performance. Combined with the state-of-the-art sensing, communication and big data analytics platforms, a systematic Industrial AI methodology will allow integration of physical systems with computational models. The concept of Industrial AI is in infancy stage and may encompass the collective use of technologies such as Internet of Things, Cyber-Physical Systems and Big Data Analytics under the Industry 4.0 initiative where embedded computing devices, smart objects and the physical environment interact with each other to reach intended goals. A broad range of Industries including automotive, aerospace, healthcare, semiconductors, energy, transportation, mining, construction, and industrial automation could harness the power of Industrial AI to gain insights into the invisible relationship of the operation conditions and further use that insight to optimize their uptime, productivity and efficiency of their operations. In terms of predictive maintenance, Industrial AI can detect incipient changes in the system and predict the remains useful life and further to optimize maintenance tasks to avoid disruption to operations.

Section 5.5 Open Source Industrial Big Data Competitions

This chapter contains nine industrial big data challenges in the United States from 2008 to 2017 and summarizes the difficulties of the challenges and the thinking of the winners (Table 5.4). The competitions cover a wide horizontal range of industries including aerospace, rail transportation, wind power, machine manufacturing, semiconductor manufacturing, and other industries, while spanning different vertical monitoring levels.

In addition, it is worth mentioning that unlike Internet big data modeling, it is difficult to find open source modeling data in industrial scenarios. All the data sets and detailed problem-solving papers collected in this chapter can be downloaded on the Internet, which will be helpful for readers to learn and practice in the field of industrial intelligent modeling.