The publications of IEEE Industrial Electronics Society: IEEE Transactions on Industrial Electronics (TIE), IEEE Transactions on Industrial Informatics (TII) and IEEE Industrial Electronics Magazine (IEM), continue to succeed. According to JCR 2019 report, there is remarkable improvement in the Impact Factors (IFs) of IEM, TII and TIE with IFs of 13.593, 9.112 and 7.515, respectively. It is the upshot of hard work, dedication, commitment, sincerity and inclusiveness of Authors, Reviewers, Associate Editors, Editors, Co-EICs and EICs towards IES publications. Congratulations to all!

To provide more benefits for our valuable members, IEEE Industrial Electronics Society has recently launched an important initiative to start IES Society-level online webinar series. This initiative is managed by IES “Multimedia Committee.” The very first two webinars were hosted in September with great success. A survey session is conducted after each webinar, and they have received excellent feedbacks! After-session survey has a participation rate of more than 65% of attendees, and more than 98% rated the webinar “Excellent” or “Very good!”

The 25th edition of the IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) was held in Vienna from 8-11 September. With a view to worldwide travel restrictions, the organizers decided to try a hybrid delivery: on site in Vienna for all those who were able to come, and online for those who did not have the possibility to travel. The conference was attended by 120 people in Vienna and about 200 online, representing 30 countries from all continents. Going hybrid was a high risk, but in the end, it was more than worth the effort!

This year, IEEE IES members Frede Blaabjerg and Rik W. De Doncker are among recipients of IEEE medals. Frede Blaabjerg received the 2020 IEEE Edison Medal "For contributions to and leadership in power electronics, developing a sustainable society," Rik W. De Doncker won the 2020 IEEE Medal in Power Engineering "For contributions to high-power and energy-conversion technologies."

Intelligent fault diagnosis is able to handle massive monitoring data and distinguish health conditions of machines, which has attracted much attention from both researchers and engineers recently. This paper proposes a new intelligent method named deep convolutional transfer learning network (DCTLN) to diagnose mechanical faults without labeled data available. The proposed DCTLN obtain higher recognition accuracies compared with other methods.