Recently, Yang Zeyu (the first author) from the School of Engineering of our University (the first unit) published a review article entitled “On Paradigm of Industrial Big Data Analytics: From Evolution to Revolution” in the international TOP journal IEEE Transactions on Industrial Informatics (TOP q1 Chinese Academy of Sciences, IF: 11.648).

The advent of intelligent manufacturing and the industrial Internet era has brought ever-increasing opportunities and challenges to modern industry. Specifically, thanks to technologies in the fields of digital, network, intelligence and industrial automation, the production model of traditional manufacturing is being transformed. As the core link between intelligent manufacturing and industrial Internet platform, industrial big data analysis has received more and more attention from both academia and industry. One of the hottest topics at present is about efficient digging of high-value information contained in industrial big data and the utilization of real industrial processes. Meanwhile, with the in-depth advancement of industrial automation to knowledge automation, the learning paradigm of industrial big data analysis is developing accordingly. Therefore, from the perspective of industrial big data analysis, the article actively explores the change of learning paradigm in the context of industrial big data in view of corresponding industrial scenarios: a) Analyzing the evolution of industrial big data analysis paradigm, i.e., from isolated learning to lifelong learning, and further summarizing the relationship between the two; b) listing the mainstream directions of lifelong learning and detailing their application in industrial scenarios; c) Looking into the future development directions.

Thesis link：https://ieeexplore.ieee.org/abstract/document/9827608