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No more meetings until January due to the holiday season

18.12.2020

Professor

Shen Yin

Please welcome our new Professor Shen Yin. Prof. Shen Yin - Self Introduction

About Speaker: Shen Yin received the B.E. degree in Automation from the Harbin Institute of Technology, Harbin, China, and the M.Sc. degree in Control and Information Systems and the PhD. (Dr.-Ing.) degree in Electrical Engineering and Information Technology from the University of Duisburg–Essen, Germany.
Dr. Yin prompted to Full Professor from December 2014 at Harbin Institute of Technology, China. He joined Department of Mechanical and Industrial Engineering, NTNU, as DNV-GL Professor from October 2020. His research interests include safety, reliability of complicated systems, system and control theory, data-driven and machine learning approaches, applications in large-scale systems and industrial cyber-physical systems.

04.12.2020

Ph.D. Candidate

Tom Iva Pedersen

Title: Model for implementation of predictive maintenance in an Industry 4.0 context (in the offshore oil and gas industry)

Abstract: It is now almost ten tears since the concept of Industry 4.0 first was introduced. The basic premise of Industry 4.0 it that the instruction of Internet of Things (IoT) and cloud computing in the manufacturing sectors will lead to a fourth industrial revolution. Most of the literature so far has focused on technological aspects. But for companies to be able to reap the benefits of Industry 4.0 there is also a need for models and frameworks on how this concept can be implemented from a managerial perspective. This paper focus on maintenance and predictive maintenance is often one of the first practical applications of Industry 4.0 to be mentioned. While many claims have been made on the potential improvements related to maintenance that can be achieved from implementing Industry 4.0, there have so far been limited empirical evidence to support these claims. There are signs that industry actors are struggling to understand the Industry 4.0 concepts, and that predictive maintenance is difficult to implement in practice. To help guide industry in the implementation of Industry 4.0 in maintenance this paper proposes a framework for how this can be done. The model focus on the underlying principle of Industry 4.0: system integration and using real-time data to take faster and better decisions and use principles from systems engineering, lean and TPM.

Opponents: Ph.D. candidateJon Martin Fordal, Endre Sølvsberg

17.11.2020

Ph.D. Candidate

Ewa Maria Laskowska

Title: Predictive Maintenance

Title:New Ph.D. candidate and postdoctoral fellow self-introduction

Xingheng Liu (postdoctoral fellow)

Malik Mohsin Abbas

Tianqi Sun

Endre Sølvsberg

Yixin Zhao

Muhammad Gibran Alfarizi