Dr. Jay Lee
Ohio Eminent Scholar, L.W. Scott Alter Chair, and Distinguished Univ. Professor Univ. of Cincinnati &
Director NSF Multi-Campus Industry/University Cooperative Research Center on Intelligent Maintenance Systems (IMS), Univ. of Cincinnati, Univ. of Michigan, Missouri Univ. of S&T and Univ. of Texas-Austin
Subject: ”Design of CPS-based Digital-Twin PHM in Future Industrial Systems”
In today’s competitive business environment, companies are facing challenges in dealing with big data issues for rapid decision making for improved productivity. Many manufacturing systems are not ready to manage big data due to the lack of smart analytics tools. U.S. has been driving the Cyber Physical Systems (CPS), Industrial Internet, and Advanced Manufacturing Partnership (AMP) Program to advance future manufacturing. Germany is leading a transformation toward 4th Generation Industrial Revolution (Industry 4.0) based on Cyber-Physical Production System (CPPS). China has just launched 2025 Plan and Internet Plus to focus on strengthening manufacturing and accelerate service innovation. It is clear that as more predictive analytics software and embedded IoT are integrated in industrial products and systems, predictive technologies can further intertwine intelligent algorithms with electronics and tether-free intelligence to predict product performance degradation and autonomously manage and optimize product service needs.
The presentation will address the trends of predictive big data analytics and CPS for future industrial PHM systems. First, Cyber-Physical System (CPS) enabled PHM will be introduced. Second, advanced predictive analytics technologies for smart maintenance with case studies will be presented. Finally, a Digital-Twin PHM platform for a closed-loop product design will be discussed.
Prof. Jerome Antoni
Laboratoire Vibrations Acoustique, University of Lyon, France
Subject: "Diagnostic indicators in vibration-based Condition Monitoring: connections between cyclostationarity, higher-order statistics, and sparsity"
Vibration-based Condition Monitoring of machines strongly relies on advanced signal processing tools. The currently available panel of cutting edge methods reflects several historical trends, driven by research in higher-order statistics, time-frequency and time-scale representations, cyclostationarity, and more recently sparsity, to cite a few. A common goal of these approaches is to extract the diagnostic information from the signals of interest in the form of a nonlinear or a nonstationary signature. The object of this presentation is twofold.
First, it intends to evidence new links between diagnostic criteria based on higher-order statistics (such as the kurtosis and its extensions like the spectral kurtosis), sparsity (on which the literature is rapidly growing) and cyclostationarity (such as the envelope spectrum). In particular, it is shown that the cyclostationary framework allows a very sparse representation of signals and contains the same information as delivered by the kurtosis and some measures of sparsity.
Next, based on these findings, a renewed interest is given to cyclostationarity with an effort to push back its current limitations. It is generalized to the case of machines operating under varying regime and a fast estimator of the Spectral Correlation Density – a key tool in cyclostationarity -- is also introduced, from which new diagnostic indicators are derived. Eventually, several examples of applications are presented in different areas of Condition Monitoring, including the automotive, aeronautic, and energy industries.
Prof. Steven Li
Department of Industrial Engineering and Engineering Management, Western New England University
Subject: "Issues and Challenges of Bayesian Inference in PHM: Prior, Data, or Lying"Abstract
Prof. Yaguo Lei
School of Mechanical Engineering, Xi’an Jiaotong University
Subject: "Machinery Health Monitoring and Intelligent Fault Diagnosis in Big Data Era"Abstract
Prof. Piero Baraldi
Energy Department, Polytechnic of Milan
Subject: "Prognostics and Health Management in the Energy Industry "Abstract
As energy is directly related with all industrial activities, improving the reliability of energy delivery while reducing costs is a key issue in the global economy. Inevitably, then, the use of PHM has attracted great interest from industries involved in the production, transportation, distribution and sale of energy.
This keynote lecture will present the specific desiderata of PHM systems for the energy industry. This will be done with reference to the typical problems addressed by PHM, i.e. detecting incipient failures, classifying their causes, predicting the system Remaining Useful Life (RUL), with its corresponding uncertainty, and considering the exploitation of the PHM outcomes for performing condition-based and predictive maintenance. By way of examples of application, the main challenges towards the deployment of PHM systems and their effective integration in the operation and maintenance of energy industries will be discussed.
Prof. Chen Yunxia
School of Reliability and Systems Engineering, Beihang University
Subject: "New challenges of reliability technology in New Energy Battery Industry "Abstract
Mr. Russell Morris
Subject: " Impact of PHM on Systems Reliability and Life Cycle Cost "Abstract