...
NB: The seminars are also on Teams, feel free to join.
Program 2021 Autumn
| When | Who | What |
|---|---|---|
14.10.2021 12:00-13:00 | IEEE Reliability Society, Sweden and Norway |
Joint Section Chapter | There is no RAMS seminar this week. Instead, you can attend the Kick-off & Webinar of IEEE Reliability Society, Sweden and Norway |
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 |
Program 2021 Spring
| When | Who | What |
|---|---|---|
- | - | No more meetings until September due to the holiday season |
10.06.2021 (Digital only) | Ph.D. Candidate Renny Arismendi | Title: Piecewise Deterministic Markov Process (PDMP) as a modelling framework for RAMS problems Abstract: A PDMP is a general class of non-diffusion stochastic models that provides a framework for studying optimization problems. It is a hybrid model with evolution that combines deterministic motion and random jumps. This presentation gives an overview of the formalism of PDMP with focus on RAMS applications such as Dynamic Reliability and Condition-based maintenance. |
03.06.2021 (Digital only) | Ph.D. Candidate Yixin Zhao | Abstract: Many critical systems with dependencies do not collapse immediately due to single-point failures but are more vulnerable to the cascading effects of these failures. Condition-based maintenance (CBM) has been found useful not only in improving availability of technical system but also in reducing the risks related to unexpected breakdowns, including those events related to dependencies, such as cascading failures. The serious disasters created by such failures and increased requirements for CBM policy due to dependencies urges a comprehensive study on current research and future challenges. In this study, a systematic literature review on the implementations of CBM in the systems with dependencies is conducted. Relevant papers are deliberately selected and analyzed in the VOSviewer program, to identify co-occurrences of keywords and so to illustrate basic concepts of CBM. Specifically, considering various types of dependencies, challenges, research advancements and research perspectives are identified. Opportunities of CBM for improving availability and reducing risks of dependent systems are finally explored. About Speaker: Yixin Zhao has a B.E. and M.E. in Safety Engineering and Science from China. She started her Ph.D. in Octorber 2020 at RAMS group, MTP, NTNU, with main supervisor Yiliu Liu and co-supervisor Jørn Vatn. Opponents: Ph.D. Candidate Lin Xie, Renny Arismendi |
11.05.2021 (Digital only) | Ph.D. Candidate Muhammad Gibran Alfarizi | Abstract: This work illustrates a data-driven approach adopted to address the PHME2021 Data Challenge competition. The aim of the challenge was to perform fault detection, classification, and root cause identification for a manufacturing production line setup. Data has been acquired under fault-free operating conditions and with the support of domain expertise, data has also been generated with a variety of seeded faults under controlled conditions. In this paper, we describe the approach followed to assess the problem and to generate robust and adaptable prediction models together with a corresponding performance assessment and robustness evaluation. About Speaker: Muhammad Gibran Alfarizi has a B.Sc. in Petroleum Engineering from Indonesia and a M.Sc. in Petroleum Engineering from NTNU. His research interest include data driven modeling and machine learning. He started his PhD in August 2020. The project title of his Ph.D. is The Digital Transformation and Data-Driven Methods in the Reliability of Safety Systems. |
29.04.2021 (Digital only) | Researcher Shenae Lee | Title: Dynamic risk analysis from the perspective of life cycle approach in IEC 61508 and IEC 61511 (*The paper is to be presented at ICheaP15 conference in May) Abstract: Dynamic risk analysis (DRA) aims to provide updated risk levels during operations of a hazardous facility. One of the main objectives of performing a DRA is to support day-to-day operational decisions, primarily for preventing major accidents. For this reason, many DRA methods have been developed to include information about the status of the safety barriers whose failures can increase the likelihood of a major accident. However, DRA is not widely used in industry, and there is no standard that describes DRA approaches and their applications. It may therefore be of interest to consider similar concepts and methods addressed in the existing standards. This paper focuses on a specific type of safety barriers, safety instrumented systems (SISs), and recognized functional standards IEC 61508 and IEC 61511 that give performance requirements to a SIS. In particular, SIS performance monitoring in the operational phase according to IEC 61508/61511 can provide valuable inputs to DRA applications. About Speaker: Shenae Lee is currently a researcher at MTP, NTNU. She finished her Ph.D. at RAMS group, MTP last year. She has B.Sc. in Nanotechnology from South Korea and M.Sc. in RAMS, NTNU. Opponents: Ph.D. Candidate Lin Xie, Professor Jørn Vatn |
15.04.2021 (Digital only) | Ph.D. Candidate Tianqi Sun | Abstract: As a vital element of the Norwegian road network, a total of over 18,000 bridges are distributed across Norway. Their maintenance strategy can be classified as condition-based, but not predictive. Periodic inspections are carried out based on predefined rules and maintenance decisions are made based on the inspection findings. Given the large stock of bridges, it is sometimes difficult to follow all inspection plans due to the limited budget and resources. An optimized inspection strategy is therefore of great value to conduct fewer inspections without increasing the risk. In this paper, a modelling framework to incorporate condition-based inspections in the multi-state Markov process is proposed. In view of the current practice when carrying out the repairs, the modelling of deterministic maintenance delays is also investigated. A case study is carried out based on empirical data from the Norwegian Public Roads Administration, the agency responsible for planning, building, operating and maintaining national and country road network in Norway. The time-dependent state probabilities and the number of repairs are calculated with both the proposed approach and Monte Carlo simulation for comparison and validation. About Speaker: Tianqi Sun has a B.E. in Safety Engineering from China and a M.Sc. in RAMS from NTNU. After almost three years work as safety engineer in a car manufacture in China, he started his Ph.D. in September 2020. The title for his Ph.D. is Strategies and Criteria for Preventive and Corrective Maintenance, which belongs to a project named SMARTer Maintenance between Statens Vegvesen and NTNU. Opponents: Ph.D. Candidate Renny Arismendi, Ewa Maria Laskowska |
08.04.2021 (Digital only) | Ph.D.Candidate Lin Xie | Abstract: Cascading failures may occur for many technical systems in situations where one component's failure triggers successive failures. The failure propagations can result in extensive damages to the systems. One can stop the propagations if safety instrumented systems act upon demands to prevent it. The demands have prolonged durations and expose safety instrumented systems to high stresses in some cases. Such stresses cause an increase in failure probabilities of safety instrumented systems. The failures during demands should be considered in the reliability analysis of safety instrumented systems. The traditional reliability measures are applicable for the failures on demands rather than the failures during demands. This paper proposes a new method to model safety instrumented systems to prevent cascading failures, considering the failures on demands and the failures during demands. It is a recursive aggregation method based on reliability block diagrams. Monte Carlo simulations are also employed to verify the method, and the applications are illustrated with a practical case study. About Speaker: Lin Xie is a Ph.D. Candidate at the RAMS group. Opponents: Professor Jørn Vatn, Postdoctoral fellow Xingheng Liu |
18.03.2021 (Digital only) | Ph.D. Candidate Nanda Anugrah Zikrullah | Title: Finite-state automata modeling pattern of systems-theoretic process analysis results Abstract: Hazard analysis using Systems-Theoretic Process Analysis (STPA) can capture a more comprehensive type of hazards, including those caused by dependent and interacting elements. Nevertheless, the method has not been utilized widely in practice. One of the challenges is STPA's lack of guidance for the follow-up evaluation process, especially for transforming qualitative requirements into the quantifiable domain. A modeling approach of STPA results based on the framework for reliability, availability, and maintainability has been proposed. However, the approach lacks a modeling pattern, resulting in uncertainties in the model's conversion process. This paper's contribution is to improve the modeling approach and to provide modeling patterns for STPA results. The modeling patterns are classified into the controlled process model and the control element model. A simple study case application is performed, and sensitivity analysis results are presented to demonstrate the model's ability to prioritize essential scenarios. About Speaker: Nanda Anugrah Zikrullah has a B.Sc. in Engineering Physics from Indonesia and an M.Sc. in RAMS from NTNU. He started his Ph.D. candidate work at RAMS group, MTP, NTNU in August 2018 and will finalize his work this autumn 2021. The Ph.D. project is under a joint-industry research project called Safety 4.0, which is lead by DNV and includes several other companies and universities. His Ph.D. working title is "Safety demonstration of novel subsea technologies". |
04.03.2021 (8:30 - 9:30, Digital only) | AutoPRO Norway-China Project Seminar | Abstract: Digital Twin (DT) constitutes to be an important pillar for industrial transformation to digitalization. Both academics and industries have recently started the exploration on methodologies and techniques related to DT. A systematic overview on the relationships and differences between DT and traditional approaches, such as simulation, is thus needed. This paper aims to contributes towards better understanding of DT, by reviewing different DT types in an effort for their grouping and classification. Subsea production is the focusing industry in this study, where conventional corrective/age-based maintenance is shifting towards condition-based maintenance (CBM) and prognostics and health management (PHM). DT is believed to be meaningful to improve efficiencies and reduce costs of such activities, but technical difficulties of DT-based PHM are existing to impede real-world applications. We outline some of these opportunities and identify challenges of DT-based PHM with an aim of highlighting future research perspectives. About Speaker: Ph.D. Candidate Malik Mohsin Abbas |
18.02.2021 (Digital only) | Ph.D. Candidate Bahareh Tajiani | Title: Mathematical Modeling for Remaining Useful Life Prediction of Bearings Abstract: Roller bearings are critical components in rotating machinery and many failures are induced by abnormalities in the bearings. In this research, a framework will be presented using empirical wavelet transform (EWT) for fault detection and HI construction, combining with Bayesian inference approach for remaining useful life (RUL) estimation. The datasets are collected by conducting some accelerated life tests in RAMS laboratory. About Speaker: Bahareh Tajiani has a BSc in Industrial Engineering from Iran and a MSc in RAMS from NTNU. She started her work as a PhD candidate at RAMS group, MTP, NTNU in August 2019. Her PhD working title is mathematical modeling for remaining useful life (RUL) prediction of bearings which is an internal project at NTNU. Opponents: Professor Shen Yin, Ph.D. candidate Tom Ivar Pedersen |
04.02.2021 | Ph.D. Candidate Aibo Zhang | Abstract: Modern industries are developing towards a high-integrated direction with overwhelming complexities bringing benefits and potential risks with catastrophic consequences simultaneously. To reduce the occurrences of undesired events or mitigate their consequences, safety-instrumented systems (SISs), as a type of technical safety barrier, have been widely installed in different applications with the aim to protect people, the environment, and other valued assets. Many SISs operate in a demanded mode, meaning that they are only activated to perform safety functions while the unexpected occurs. For such systems, it is important to conduct proof tests for checking system states and following-up maintenance in case of failures, to keep SISs highly available so as to ensure safety. In current studies, these activities are assumed following a predefined scheme with fixed intervals, independent from the actual system state. However, when more SIS state information can be collected by sensors and in manual tests, the prognostics and health management (PHM) strategy is expected to be more reasonable and cost-efficient. This PhD project thus aims to explore a new approach to evolve the SISs management from time-based to performance-based taking the technological advancement in data collection. The thesis bridges SISs performance assessment and degradation process through addressing different influence factors in the operational phase, including aging, and impact of demands, etc, for the decision-making in PHM. The practical utility of the thesis resides in the provision of a comprehensive consideration of the time- and event-dependencies of SIS performance, as well as safety and economic meanings of testing and maintenance activities. In particular, the first is to provide hints of system deterioration and relevant health management to reliability analysts when they evaluate SIS design. The second is for operational managers of SISs as the decision-makers, to help them to update testing and maintenance plans and identify the optimal intervention opportunities. About Speaker: Aibo Zhang is a Ph.D. candidate at RAMS group, MTP, NTNU, with main supervisor of Yiliu Liu and co-supervisor Anne Cecile Barros. He will have his defence on Feb. 10th. |
21.01.2021 (Digital only) | Postdoc Xingheng Liu | Title: Remaining useful life prediction for subsea choke valves: degradation mechanism and candidate models Abstract: Subsea systems are prone to degradations and failures since they are located in a harsh environment. Due to the inaccessibility, field inspections and maintenance on subsea systems are generally complex, expensive, and cannot be carried out without a certain delay. Thus, predicting the systems' remaining useful lifetime (RUL) bears a recognized value in industrial facilities' safety and economic aspects. In this presentation, we address RUL prediction for subsea choke valves, which are used to control the flow rate of liquid and gas mixtures and prevent the equipment from unusual pressure fluctuations. After introducing the degradation mechanism, we shall present the health indicator, the needed data, and some candidate models. Finally, an example of a primitive prototype will be demonstrated. 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. |
...