...
| When | Who | What |
|---|---|---|
14.10.2021 12:00-13:00 | IEEE Sweden and Norway, Reliability Society Joint Section Chapter | There is no RAMS seminar this week. Instead, you can attend the Kick-off & Webinar of IEEE Sweden and Norway, Reliability Society Joint Section Chapter. The agenda is as follows:
Speakers: Prof. Min Xie was awarded a scholarship to pursue his undergraduate study in Sweden in 1979. After graduating from KTH, he continued with his PhD study under the supervision of Prof Bo Bergman at Linköping University, and completed his PhD in 1987. He has been involved in IEEE Reliability Society activities since then, and was elected IEEE Fellow in 2005. Prof. Xie moved to National University of Singapore in 1991 and then to City University of Hong Kong in 2011. He has published over 300 journal papers and several books, and guides over 50 PhD students. Dr. and Adjunct Prof. Pierre Dersin studied at MIT, where he obtained first a MS in Operations Research, then a Ph.D. in Electrical Engineering under Prof. Michael Athans. Since 1990, Dr. Dersin has been with Alstom (St-Ouen, France), mainly active in Reliability Engineering and Maintenance of Railway Systems. In particular, he founded there the “RAM Center of Excellence”. He started Alstom’s Predictive Maintenance Program. He is currently Prognostics & Health Management (PHM) Director in Alstom’s Digital & Integrated Systems Division, as well as ‘ RAM Master Expert’. He is the author of many publications in Reliability, Automatic Control, and Power Systems, including four chapters the “Handbook of RAMS in Railways: Theory & Practice” (CRC Press,Taylor & Francis), 2018. Pierre Dersin is a member of the IEEE Reliability Society AdCom and leads the Technical Committee on Systems of Systems. |
30.09.2021 (Digital only) | Yuchen Jiang and Shimeng Wu from Harbin Institute of Technology | Title: Remaining useful life prediction based on machine learning approaches Abstract: Digital transformation and digital twin technologies have facilitate the connection, the interaction, and the in-depth integration of the physical space and the virtual digital space. Colossal amount of process data are available and waiting to be made use of. In light of this, the safety and reliability challenges in industrial systems are likely to usher novel solutions. In this talk, we will share our recent work on system-level RUL prediction based on machine learning approaches. The work is practical problem-driven and therefore the talk will focus on analysis of the practical problem we study, the data-related problems, the feature exploitation tools, and the ideas of how to achieve accurate RUL prediction. Finally, a short discussion will be made about the pros and cons of ML-based and traditional model-based approaches. |
16.09.2021 (Digital only) | PhD Jie Liu, Wanwan Zhang | Title:New Ph.D. Candidate Self-introduction |
02.09.2021 (Digital only) | Postdoc Xingheng Liu | Title: Modeling choke valve erosion with dynamic system Abstract: Choke valve erosion is a major issue encountered in subsea oil production. When the valve is severely eroded, production needs to be slowed down or shut off to reduce the risk of major incidents such as leakage. Visual inspection is hardly possible for subsea equipment and therefore the monitoring of a choke valve installed at an X-mas tree/manifold relies usually on the monitoring of a degradation indicator, Cv (flow coefficient). The Cv indicates a valve's capability to let the fluid flow through a choke. Theoretically, the Cv increases monotonically at a given opening (valve travel/lift) with the erosion. The issue with using Cv as a degradation indicator is that the valve is operated at a non-constant opening. Consequently, plotting Cv against time will lead to a non-monotonic Cv curve. Inappropriate use of the recorded Cv sequences can therefore give misleading results about the degradation level and erosion rate. We propose to use a dynamic system to model the choke valve erosion. The hidden state evolving over time is the effective flow area (EFA) that becomes larger as the valve is eroded. The system model shows how the hidden states evolve each day and is established based on physical models (erosion response model, fluid mechanics equations) and Gamma process (for intrinsic erosion growth randomness). The observation model shows the relationship between observed Cv and the effective flow area, accounting for measurement noise. With some field data (daily allocated flow rate, sand rate, valve opening) and choke valve features (flow characteristics, Cv curve), we can estimate the model parameters and predict the erosion growth and remaining useful life of the valve. About Speaker: Xingheng Liu is a postdoctoral fellow at RAMS group, MTP, NTNU. His research topic is Prediction and optimization of remaining useful lifetime, which is a part of SUBPRO project. He completed his cotutelle Ph.D. in ROSAS (department of Operational Research, Applied Statistics and Simulation) at the University of Technology of Troyes (fr) and in RAMS at NTNU and earned his Master's and Bachelor's degree in Industrial Engineering at UTT. |
19.08.2021 | Ph.D. Candidate Endre Sølvsberg | Abstract: As part of the EU project Qu4lity, the Continental pilot “Autonomous Quality in PCB Production for Future Mobility” involves PCB (Printed Circuit Board) production in an SMD (Surface-Mount Device) line at their site in Sibiu, Romania. The main issue is faulty PCBs passing all in-line testing, and being sent out to customers, resulting in a high number of customer returns. The Pilot team has cooperated with domain expertise on-site in Romania to identify critical variables from the Continental Datalake and employed a PCA approach using the Mahalanobis Distance to identify customer returns as outliers and enabling the quality team at Sibiu to identify faulty PCBs before they are sent out to customers. Using a Docker container, the PCA algorithm has been automated with a Python script. An integrated GUI enables the quality team and other relevant people to see PCBs that are tagged by the algorithm as outliers, in addition to check individual unit ID tags. Through 5 months of PCB production, there have been 171 customer returns, and the quality team at Sibiu has validated that the algorithm was able to identify 169 of these PCBs as outliers, close to 99% of all customer returns in the 5 month period. About Speaker: Endre Sølvsberg has a BA in Economics and an MSc in Sustainable Manufacturing. He has lectured at NTNU in the Masters level course Scientific Methodology. He has worked at SINTEF Manufacturing as a project engineer and researcher, in projects involving I4.0, ZDM and Smart Maintenance. He has served as the technical lead for SINTEF in the Continental Pilot in the EU Quality project. The working title of his PhD project is “Extending lifetime of Norwegian oil installations using predictive maintenance through condition and remaining useful life estimation", and the project is sponsored by OKEA and part of the NTNU program BRU21. Opponents: Harald Rødseth, Ph.D. candidateJon Martin Fordal |
...