IMS Center Papers Have the Highest Number of Citations in CIRP, MSSP and Manufacturing Letters (UPDATED)

Professor Jay Lee and members of the IMS Center research team hold the record for the most citations for a single paper in three high-profile publications: Manufacturing Letters, the Proceedings of the 6th CIRP Conference on Industrial Product-Service Systems, and the Mechanical Systems and Signal Processing (MSSP) Journal.

Please find below the links to the publications:

A Cyber Physical Systems Architecture for Industry 4.0-based Manufacturing Systems

Manufacturing Letters, Volume 3, January 2015, 18-23

Recent advances in manufacturing industry has paved way for a systematical deployment of Cyber-Physical Systems (CPS), within which information from all related perspectives is closely monitored and synchronized between the physical factory floor and the cyber computational space. Moreover, by utilizing advanced information analytics, networked machines will be able to perform more efficiently, collaboratively and resiliently. Such trend is transforming manufacturing industry to the next generation, namely Industry 4.0. At this early development phase, there is an urgent need for a clear definition of CPS. In this paper, a unified 5-level architecture is proposed as a guideline for implementation of CPS.


Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment

Proceedings of the 6th CIRP Conference on Industrial Product-Service Systems, Volume 16, 2014, Pages 3-8, Ontario, Canada.

Today, in an Industry 4.0 factory, machines are connected as a collaborative community. Such evolution requires the utilization of advance- prediction tools, so that data can be systematically processed into information to explain uncertainties, and thereby make more “informed” decisions. Cyber-Physical System-based manufacturing and service innovations are two inevitable trends and challenges for manufacturing industries. This paper addresses the trends of manufacturing service transformation in big data environment, as well as the readiness of smart predictive informatics tools to manage big data, thereby achieving transparency and productivity.


Prognostics and Health Management Design for Rotary Machinery Components

International Journal of Mechanical Systems and Signal Processing, January 2014, 42(1-2):314-334.

Much research has been conducted in prognostics and health management (PHM), an emerging field in mechanical engineering that is gaining interest from both academia and industry. Most of these efforts have been in the area of machinery PHM, resulting in the development of many algorithms for this particular application. The majority of these algorithms concentrate on applications involving common rotary machinery components, such as bearings and gears. Knowledge of this prior work is a necessity for any future research efforts to be conducted; however, there has not been a comprehensive overview that details previous and on-going efforts in PHM. In addition, a systematic method for developing and deploying a PHM system has yet to be established. Such a method would enable rapid customization and integration of PHM systems for diverse applications. To address these gaps, this paper provides a comprehensive review of the PHM field, followed by an introduction of a systematic PHM design methodology, 5S methodology, for converting data to prognostics information. This methodology includes procedures for identifying critical components, as well as tools for selecting the most appropriate algorithms for specific applications. Visualization tools are presented for displaying prognostics information in an appropriate fashion for quick and accurate decision making. Industrial case studies are included in this paper to show how this methodology can help in the design of an effective PHM system.


Recent Advances and Trends in Predictive Manufacturing Systems in Big Data Environment

Manufacturing Letters, Volume 1, Issue 1, October 2013, 38-41

The globalization of the world’s economies is a major challenge to local industry and it is pushing the manufacturing sector to its next transformation – predictive manufacturing. In order to become more competitive, manufacturers need to embrace emerging technologies, such as advanced analytics and cyber-physical system-based approaches, to improve their efficiency and productivity. With an aggressive push towards “Internet of Things”, data has become more accessible and ubiquitous, contributing to the big data environment. This phenomenon necessitates the right approach and tools to convert data into useful, actionable information.


For more IMS Center publications, please visit our publications page here.


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Professor Jay Lee