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Jay Lee

Bio: Jay Lee is a academic researcher from University of Cincinnati. The author has contributed to research in topic(s): Prognostics & Fault (power engineering). The author has an hindex of 57, co-authored 345 publication(s) receiving 19221 citation(s). Previous affiliations of Jay Lee include Shanghai Jiao Tong University & University of Wisconsin–Milwaukee.

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Papers
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Journal ArticleDOI: 10.1016/J.MFGLET.2014.12.001
Jay Lee1, Behrad Bagheri1, Hung-An Kao1Institutions (1)
Abstract: 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.

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Topics: Cyber-physical system (60%), Industry 4.0 (55%), Analytics (52%)

2,716 Citations


Open accessJournal ArticleDOI: 10.1016/J.PROCIR.2014.02.001
Jay Lee1, Hung An Kao1, Shanhu Yang1Institutions (1)
01 Jan 2014-Procedia CIRP
Abstract: 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.

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Topics: Big data (58%), Analytics (57%), Industry 4.0 (55%) ...read more

1,229 Citations


Journal ArticleDOI: 10.1016/J.YMSSP.2013.06.004
Jay Lee1, Fangji Wu2, Wenyu Zhao1, Masoud Ghaffari1  +2 moreInstitutions (2)
Abstract: 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.

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Topics: Prognostics (63%)

895 Citations


Journal ArticleDOI: 10.1016/J.JSV.2005.03.007
Hai Qiu1, Jay Lee1, Jing Lin2, Gang Yu3Institutions (3)
Abstract: De-noising and extraction of the weak signature are crucial to fault prognostics in which case features are often very weak and masked by noise. The wavelet transform has been widely used in signal de-noising due to its extraordinary time-frequency representation capability. In this paper, the performance of wavelet decomposition-based de-noising and wavelet filter-based de-noising methods are compared based on signals from mechanical defects. The comparison result reveals that wavelet filter is more suitable and reliable to detect a weak signature of mechanical impulse-like defect signals, whereas the wavelet decomposition de-noising method can achieve satisfactory results on smooth signal detection. In order to select optimal parameters for the wavelet filter, a two-step optimization process is proposed. Minimal Shannon entropy is used to optimize the Morlet wavelet shape factor. A periodicity detection method based on singular value decomposition (SVD) is used to choose the appropriate scale for the wavelet transform. The signal de-noising results from both simulated signals and experimental data are presented and both support the proposed method.

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Topics: Wavelet packet decomposition (76%), Stationary wavelet transform (71%), Wavelet (71%) ...read more

835 Citations


Journal ArticleDOI: 10.1016/J.MFGLET.2013.09.005
Abstract: 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.

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Topics: Big data (57%), Analytics (56%), Emerging technologies (51%)

696 Citations


Cited by
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Open access
Christopher M. Bishop1Institutions (1)
01 Jan 2006-
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

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Topics: Kernel method (60%), Kernel (statistics) (60%), Graphical model (58%) ...read more

10,141 Citations



Journal ArticleDOI: 10.2307/2348117
Andrew B. Lawson1, Noel A CressieInstitutions (1)
01 Mar 1993-The Statistician
Topics: Spatial analysis (61%)

6,278 Citations



Journal ArticleDOI: 10.1016/J.YMSSP.2005.09.012
Abstract: Condition-based maintenance (CBM) is a maintenance program that recommends maintenance decisions based on the information collected through condition monitoring. It consists of three main steps: data acquisition, data processing and maintenance decision-making. Diagnostics and prognostics are two important aspects of a CBM program. Research in the CBM area grows rapidly. Hundreds of papers in this area, including theory and practical applications, appear every year in academic journals, conference proceedings and technical reports. This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing CBM with emphasis on models, algorithms and technologies for data processing and maintenance decision-making. Realising the increasing trend of using multiple sensors in condition monitoring, the authors also discuss different techniques for multiple sensor data fusion. The paper concludes with a brief discussion on current practices and possible future trends of CBM.

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3,419 Citations


Performance
Metrics

Author's H-index: 57

No. of papers from the Author in previous years
YearPapers
202116
202047
201916
201823
201714
20169

Top Attributes

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Author's top 5 most impactful journals

Mechanical Systems and Signal Processing

12 papers, 1.8K citations

IFAC Proceedings Volumes

11 papers, 164 citations

Journal of Sound and Vibration

8 papers, 1.2K citations

Manufacturing letters

7 papers, 3.8K citations

Procedia Manufacturing

7 papers, 34 citations