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NB: The seminars are also on Teams, feel free to join.
Program 2022 Spring
| When | Who | What |
|---|---|---|
24.02.2022 15:00 - 18:00 | Ph.D. Candidate Nanda Anugrah Zikrullah | There will be no seminar this week. Instead, you are invited to Nanda Anugrah Zikrullah’s defense.
More information can be found here. |
09.02.2022 10:30-11:30 | Guest Ph.D Candidate Danilo Colombo | Title: Optimizing the testing policy for the Blowout Preventer Abstract: The topic of the presentation is the optimization of the testing policy of a subsea Blowout Preventer (BOP). The subsea BOP is a safety-critical equipment used during the construction or intervention in a well. It is installed at the top of the wellhead, near the seabed, and connects the well with the rig via riser. When a kick occurs, i. e., the formation fluids start to flow into the wellbore, the BOP is activated and acts like a valve, sealing the well and preventing an oil spill from occurring. To ensure its availability and safety, the BOP is periodically tested, which entails the operation to be stopped. The tests are done usually according to the best practices (e.g. regulations, standards). The downtime due to the testing period may have a significant economic impact. The aim of this study is to optimize the test strategy for BOP reducing costs while satisfying the integrity lever required. To do so it will be considered three kinds of tests: (i) functional tests, (ii) partial pressure tests; and (iii) maximum pressure tests. The study will investigate a formulation for the test coverage and costs of each test. The test policy should consider the last overhaul of the BOP (i.e., the age of components) and failures that lead to a loss of redundancy in the system, which affects the probability of having a safety impact. Possible future developments are: (i) to include the degradation caused by the test; (ii) to consider dependent failures. About Speaker: Guest Ph.D Candidate from Brazil, Danilo Colombo. He is a mechatronics engineer and obtained a M.Sc in Production Engineer with the work in Markov chains to model the subsea well integrity. He is a petroleum engineer at Petrobras and he is currently an advisor in reliability and risk analysis at the CENPES Research Center. He is a member of SPE (Society of Petroleum Engineers) and ABRISCO (Brazilian Association of Risk, Reliability and System Safety). |
| 27.01.2022 | Ph.D. Candidate Lin Xie | There will be no seminar this week. Instead, you are invited to Lin Xie’s defense.
Check the program for more information. Lin Xie_english program.pdf |
13.01.2022 (Digital only) | Ph.D. Candidate Lin Xie | Title: Safety barriers in complex systems with dependent failures—Modeling and assessment approaches Abstract: Technical systems are becoming more and more complex with a degree of dependencies. Such dependency issues can significantly reduce system reliability and cause catastrophes without proper prevention. Therefore, a variety of control measures, such as safety barriers, are necessary to be adopted against dependent failures and ensure the safety of technical systems. However, in the current literature, neither the effects of dependent failures within safety barriers nor the impact of safety barriers against dependent failures has been well studied. Therefore, it is desirable to analyze and model the effects of safety barriers in complex systems considering dependency issues, such as dependency between safety barriers and the environment, dependent failures within safety barriers, and safety barriers against dependent failures. The Ph.D. thesis bridges safety barriers and complex systems by considering the dependency issues. The aim is broken into four objectives addressed in five journal articles and three conference articles. |
Program 2021 Autumn
| When | Who | What |
|---|---|---|
- | - | No more meetings until January due to the holiday season |
09.12.2021 (Digital only) | Professor Shen Yin | Title: Robustness and Sensitivity of AI systems - Two Sides of a Coin Abstract: In recent years, artificial intelligence has made remarkable breakthrough, where a large number of AI-enabled systems have been developed and applied in manufacturing industry, medical care, cyber security, and many other fields. An interesting phenomenon lies in the different design targets of AI models – some should be robust to the abnormal changes of data while the others should be another way round. For example, to predict the remaining useful life of batteries, it is favorable to develop robust AI where the model is expected to be least affected by disturbances. By contrast, the sensitivity of the model might be critical in order to diagnosis of cyber-attacks, in which the AI models should be highly sensitive to malfunctions and malicious attack behaviors, keeping effective in case of any insignificant changes. From an engineer point of view, this talk will focus on both robustness and sensitivity of AI systems, which are regarded as two sides of a coin. The formulation of several typical demand-driven examples, the design approaches, and the corresponding performance will be introduced. A balance between sensitivity and robustness of AI is worth to be considered further in the R&D phase to cope with various demands in practice. About Speaker: Shen Yin received the B.E. degree in Automation from the Harbin Institute of Technology, Harbin, China, and the M.Sc. degree in Control and Information Systems and the PhD. (Dr.-Ing.) degree in Electrical Engineering and Information Technology from the University of Duisburg–Essen, Germany. |
| 25.11.2021 | Researcher Shenae Lee | Title: An approach to update the reliability performance of safety barriers based on operating experience (The paper is to be submitted for Loss Prevention 2022) Abstract: Hazardous events in process plants like the leakage of dangerous substances can result in severe damage, and such an event is often defined as the TOP event of a fault tree analysis (FTA) in a quantitative risk analysis. The input data for a FTA are often generic reliability data that are not necessarily catered for a plant specific analysis. Therefore, this paper presents an approach based on Bayesian network (BN) analysis with a focus on Hierarchical Bayesian analysis for handling situations where plant-specific data are sparse. The suggested approach is demonstrated by a case study of a pressure vessel. About Speaker: Shenae Lee is currently a researcher at MTP, NTNU (2020-2022). She finished her Ph.D. at RAMS group, MTP in 2020. She has B.Sc. in Nanotechnology from South Korea and M.Sc. in RAMS, NTNU. |
| 11.11.2021 | Ph.D. Candidate Tom Ivar Pedersen | Title: Data-exploration and possibilities for anomality detection and RUL-prediction Abstract: A dataset of maintenance records and sensor readings from a group of water cooled power cords collected from a plant in the process industry will be presented. Two master students at NTNU used this dataset in their master thesis earlier this year. They explored the possibilities for estimating remaining useful life (RUL) for these component. As part of his Ph.D., he plans to investigate this dataset further. The goal of this presentation is to discuss how this dataset can be used as basis for a paper. He has done some preliminary data exploration and found that there are two types of failures in the dataset. One type is a gradual degradation that can be tracked with a health indicator. A preliminary plan is to use some variant of the Wiener process as basis for RUL-prognosis. |
04.11.2021 (postponed due to time conflict) | Ph.D. Candidate Ewa Maria Laskowska, Emefon Dan | Title: Maintenance Optimization of Emergency Shutdown Valves (ESV) Abstract: The topic of the presentation is the optimization of maintenance and testing policy of Emergency Shutdown Valves (ESVs). ESVs are safety critical equipment used in oil and gas facilities. They are used only in case of a demand and remain passive due to normal operation. To ensure their safety ESVs have to be periodically (full proof tests) tested what often require production shutdown and leads to faster use of these valves. It is also possible to test valves online by so-called partial stroke tests (PST). Although this kind of test doesn’t impede the production process it is less reliable than full proof tests with regard to capacity of revealing failures. The aim of this work is to find the optimal maintenance strategy for ESVs while satisfying ESVs safety requirements. The modelling framework is based on the Markov state degradation model. In such a model, the condition of ESVs is defined by discrete states. The important part of the approach is to consider condition-based inspection policy, when the testing intervals are dependent on the ESV’s condition. Two types of valves tests are considered: partial (online) tests and full proof tests. They differ with regard to the test coverage (reliability) and cost of testing. At each inspection the decision about repair is made, so that valve’s condition can be improved to a better state, or no repair is made but there is a shift in testing frequency. Also repairs have different cost assigned depending on whether they are performed online require production shutdown. Parameters or variables considered in the model: - There are 2 variables (parameters) regarding state of the valve: state and coverage. - There are 2 types of inspections: Full Poof Tests (FPT) and Partial Tests (PT). - There are 2 decision variables with regard to condition of the valve: repair decision and inspection interval. - There are 3 kind of repairs: online repairs, repairs performed during planned shutdown and repairs requiring unplanned shutdown - There are 2 aspects to consider with regard to cost of maintenance. First the cost depends on the revealed condition of the valve. Second, the cost can be accordingly increased or reduced depending on whether it requires unplanned shutdown and is performed withing a planned shutdown - There are two cost figures related to condition monitoring: cost of FPT and cost of PT Speaker: Ph.D. candidate Ewa Maria Laskowska Title: New Ph.D. Candidate Self-introduction Emefon Dan - Self introduction.pdf Speaker: Ph.D. candidate Emefon Dan Note that the physical meeting will be at Gløshaugen Materialteknisk 3. etasje Holand. |
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 NorwayJoint 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 candidate 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 (Hybrid) | 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. |
Program 2020 Autumn
| When | Who | What |
|---|---|---|
- | - | No more meetings until January due to the holiday season |
18.12.2020 (Digital only) | Professor Shen Yin | Please welcome our new Professor Shen Yin. Prof. Shen Yin - Self Introduction About Speaker: Shen Yin received the B.E. degree in Automation from the Harbin Institute of Technology, Harbin, China, and the M.Sc. degree in Control and Information Systems and the PhD. (Dr.-Ing.) degree in Electrical Engineering and Information Technology from the University of Duisburg–Essen, Germany. |
04.12.2020 (Digital only) | Ph.D. Candidate Tom Iva Pedersen | Abstract: It is now almost ten tears since the concept of Industry 4.0 first was introduced. The basic premise of Industry 4.0 it that the instruction of Internet of Things (IoT) and cloud computing in the manufacturing sectors will lead to a fourth industrial revolution. Most of the literature so far has focused on technological aspects. But for companies to be able to reap the benefits of Industry 4.0 there is also a need for models and frameworks on how this concept can be implemented from a managerial perspective. This paper focus on maintenance and predictive maintenance is often one of the first practical applications of Industry 4.0 to be mentioned. While many claims have been made on the potential improvements related to maintenance that can be achieved from implementing Industry 4.0, there have so far been limited empirical evidence to support these claims. There are signs that industry actors are struggling to understand the Industry 4.0 concepts, and that predictive maintenance is difficult to implement in practice. To help guide industry in the implementation of Industry 4.0 in maintenance this paper proposes a framework for how this can be done. The model focus on the underlying principle of Industry 4.0: system integration and using real-time data to take faster and better decisions and use principles from systems engineering, lean and TPM. Opponents: Ph.D. candidateJon Martin Fordal, Endre Sølvsberg |
17.11.2020 (Digital only) | Ph.D. Candidate Ewa Maria Laskowska | Title: Predictive Maintenance |
06.11.2020 (Hybrid) | 1 new Postdoc and 5 new PhDs | Title:New Ph.D. candidate and postdoctoral fellow self-introduction Xingheng Liu (postdoctoral fellow) |
Program 2018 Spring
| When | Who | What |
|---|---|---|
- | - | No more meetings until September due to the holiday season |
15.06.2018 | Closing social event | Thanks for your participation and support for the RAMS Seminars. We warmly welcome you to the closing social event at 11.00 AM, this Friday (15th June), at VG 11, Valgrinda. We will server: 1) A funny Quiz and the winner will receive a gift; 2) Free talks with the RAMS students, professors, and others; 3) Pizza, cakes, drinks, coffee, and snacks….. All are welcome! |
| 08.06.2018 | Associate Professor Cecilia Haskins | Title: Systems Engineering in 45 minutes Systems engineering-for-RAMS-group.pdf Abstract: A brief overview of the important aspects of systems engineering. About speaker: Cecilia had a career of 35 years as a practicing systems engineer before she began teaching the subject here in Norway. She holds a certification as an Expert Systems Engineering Professional. After earning her PhD in Systems Engineering at NTNU, she has been teaching systems engineering and systems thinking here in the department since 2010. |
| 01.06.2018 | Himanshu Srivastav, Lin Xie, Aibo Zhang, Yun Zhang | Title: Presentations for ESREL
1) Optimization of Periodic Inspection time of SIS subject to a regular Proof Testing Himanshu.pdf Speaker: Himanshu Srivastav Description: SIS is a generally passive system which is activated when the demand arises. Since they are the passive system they are not continuously monitored. Hence, periodic proof testing is arranged to check the availability of the SIS. With some systems, proof testing may damage the system and might cause deterioration. This is termed as the negative effect of testing. In this talk, we will discuss how the negative effect of proof testing can be modeled using multiphase Markov process. Opponents: Visit PhD Xiaopeng Li 2) Common Cause Failures and Cascading Failures in Technical Systems Lin Xie.pdf Speaker: Lin Xie Description: Dependent failures, such as common cause failures and cascading failures, are becoming important concerns to the system reliability. Both failure types may lead to the unavailability of multiple components at the same time or within a short time interval. Although many researchers have studied common cause failures and cascading failures respectively, there is a little comparison of the two concepts. We investigate the similarities and differences of these two failure groups, with focus on the conditions and nature of initiations and propagation of such failures. Moreover, a comparison is also made about suitable barrier strategies that can either prevent or reduce the consequences of failure. Opponents: Prof. Anne Barros
3) PHM for safety barriers in infrastructures: opportunities and challenges Aibo Zhang.pdf Speaker: Aibo Zhang Description: Different types of safety barriers are deployed in many infrastructures to reduce the occurrences of hazards, but the capacity of these barriers can be weakened by degradations or the failures related to changes over time. It is natural to adopt the approaches of prognostic and health management (PHM) to monitor the conditions and measurable parameters of safety barriers and predict their future performance by assessing the extent of degradations. This study aims to explore the uniqueness and possible challenges when implementing PHM on safety barriers due to the operational characteristics of barriers. Opponents: Prof. Mary Ann Lundteigen
4) Modeling methodology and patterns for the monitored system Yun.pdf Speaker: Yun Zhang Description: Huge rotary machines are commonly used in oil and gas processing plants for separation, compression, and boost. Their reliability is of high importance to avoid operation downtime and production loss. In this paper, a modeling methodology is presented, based on the AltaRica 3.0 modeling language and stochastic simulation, to assess the average production level of a compressor drive system. This system consists of six trains, where each of them contributes to one-sixth of the total production capacity. It runs under two operation modes (full and reduced capacity) corresponding to seasonal demand periods (winter and summer). The problem at stake is to design a model at system level that captures the various degradation processes, monitoring policies, and maintenance rules involved in the system under study. The aging of units is represented by means of multiple degradation levels. Given units information provided by monitoring and inspection, preventive and corrective maintenance interventions are decided locally to each unit. Performance indicators such as the cumulative production and production loss over a certain mission time can then be assessed. Opponents: Prof. Jørn Vatn, Ph.D. candidate Juntao Zhang |
| 25.05.2018 | Canceled | Canceled |
| 11.05.2018 | Assoc. Prof. Viggo Gabriel Borg Pedersen | Title: Monitoring data quality – Demonstration of vibration measurements Condition Monitoring data collection.pdf and Vibration.pdf Abstract: The quality of condition monitoring data affects remaining useful life prognosis based on the monitoring data. The whole measurement chain has to be customized in such a way that relevant data of the right quality is collected for analysis purposes. Choosing the right sensor for the job and installing it correctly at the optimal position require knowledge of the process and unit being monitored. Conversion and transmission of sensor data is another source of error in data acquisition systems. The presentation will highlight and discuss some issues related to the above-mentioned challenges. Demonstration of gathering vibration data on two live test rig's is part of the presentation. The rigs of the type rotational equipment are mounted in the lecture room. About speaker: Assistant Professor teaching operation & maintenance and design of piping systems at bachelor level the last five years. 10 years’ work experience in construction, operation, and maintenance of offshore installations & ships. 9 of these as an engineer in different positions. 9 years’ work experience related to hydropower, operation, and maintenance, laboratory management in different engineering positions. MSc – Marine Technology, marine machinery; MA – Project management; BSc –mechanical engineering Other relevant materials can be found here: accident analysis.pdf, the fight for the right to repair.pdf and Vibration analysis.pdf |
| 04.05.2018 | Juntao Zhang
Jon Martin Fordal | Title: Adapting Systematic Theoretic Process Analysis for Reliability Analysis Juntao Zhang_RAMS seminar_04.05.pdf Abstract: The rather recent method named System-Theoretic Process Analysis (STPA) is one promising candidate to improve the coverage of hazard identification in complex systems that involve highly coupled parts, non-linear interactions, and software-intensive functionalities. Still, there is no guideline for utilizing STPA output to evaluate the potential of loss, which is important for a basis for decision-making about system configuration and equipment selection. The focus of this article is placing on the interface between STPA and reliability, availability and maintainability (RAM) modeling. The approach named STPA-RAM modeling is proposed to translate feedback control loops into Petri-nets for discrete event simulation. The proposed approach is demonstrated with a simple case related to the subsea design concept. It has been found that the new proposed approach extends the application of STPA, while also improving, and as such reducing completeness uncertainty and model uncertainty, associated with input data and information for RAM modeling. About speaker: Juntao Zhang is a PhD candidate in RAMS group, in related to research center SUBPRO. PhD topic is incorporating reliability and availability analysis in the early design phase of subsea systems. He is under the main supervision of Prof. Mary Ann Lundteigen in RAMS group. He has a Master degree from RAMS program, NTNU. Opponents: Prof. Cecilia Haskins, PhD candidate Shenae Lee
Title: New Ph.D. Candidate Self-introduction Self_introduction_Fordal.pdf Abstract: Self-introduction of Jon Martin Fordal – background & and next steps About speaker: Jon Martin Fordal comes from Stjørdal and has a bachelor degree in mechanical engineering, master of science in industrial engineering, and has worked as a maintenance engineer in Elkem ASA. 1stofmarch he started as a PhD candidate, and will be working on the research project “CPS Plant”. The project will develop a framework for the Norwegian approach for the digital manufacturing industry. The consortium consists of 3 Norwegian industry partners, Norsk Hydro, Benteler Automotive and Hycast, and SINTEF Digital and NTNU (Trondheim and Gjovik) are the academic partners. SINTEF Raufoss Manufacturing is the project leader. |
| 20.04.2018 | Assoc. Prof. Astrid S. de Wijn | Title: Criticality in Dynamic Arrest: Correspondence between Glasses and Traffic RAMS_Astrid.pdf Abstract: The dynamic arrest is a general phenomenon across a wide range of dynamic systems including glasses, traffic flow, and dynamics in cells, but the universality of dynamic arrest phenomena remains unclear. We connect the emergence of traffic jams in a simple traffic flow model directly to the dynamic slowing down in kinetically constrained models for glasses. Using the Nagel-Schreckenberg model to simulate traffic flow, we show that the emergence of jammed traffic acquires the signature of a sharp transition in the limit corresponding to overcautious driving. We identify a true dynamic critical point marking the onset of coexistence between free-flowing and jammed traffic and demonstrate its analogy to kinetically constrained glass models. We find diverging correlations analogous to those at a critical point of thermodynamic phase transitions. About speaker: Associate Professor at Department of MTP at NTNU and also attached to the department of Physics Stockholm University. Her interest focus on statistical mechanics, tribology, condensed matter, nonlinear dynamics, surface science. More detail please check: http://www.syonax.net/science/research.html |
| 06.04.2018 | Shenae Lee
| Title: Application of Bayesian Belief Networks (BBNS) for process plant Shenae RAMS seminar.pdf Abstract: This study is about risk analysis of process plants where main accidents continue to occur. Conventional risk analysis method has the limitation of having a static structure, while another challenge is difficult to aggregate operational data from different sources. To focus on these limitations, the paper suggests an approach based on Bayesian networks (BNs), and it is illustrated by a case study of a pressure relief valve in an Ammonia plant. The approach seems to be suitable for updating frequency of accident scenarios when new risk information is collected during the operational phase. About speaker: Shenae Lee is a Ph.D. student in RAMS group. The topic for her PhD is a dynamic risk analysis of major accident hazards in process facility operations to support safety-critical decisions. She is under the main supervision of Prof. Nicola Paltrinieri in RAMS group. She has a Master’s from the international RAMS program, NTNU. Opponents: Pierluigi Salvo Rossi, Kongsberg Digital AS, Norway; Juntao Zhang, RAMS group |
| 16.03.2018 | Prof. Antoine Rauzy | Title: Reliability Analysis of Looped Systems Abstract: It is well widely assumed (and true) that reliability block diagrams are equivalent to fault trees, although this equivalence is formally established in no textbook. A reliability block diagram can be seen as an oriented graph: blocks are represented by nodes and connections between blocks by edges of the graph. This graph is oriented, contains no loop (is acyclic), has a unique source node s and a unique target node t. The system described by the reliability block diagram works if and only if there is a working s-t path in the graph, an i.e. s-t path along which all nodes are working (only nodes can fail, edges are assumed to be perfectly reliable). An interesting question is: what does happen if we accept loops, i.e. if we consider reliability networks rather than reliability block diagrams? Assessing the reliability of networks is indeed of practical importance as most infrastructures can be seen as networks. Nevertheless, reliability networks are only seldom used as a modeling tool. In this seminar, the main mathematical results explaining why it is so and present several algorithms to solve the problem will be recalled. About speaker: Antoine B. Rauzy has currently a full professor position at Norwegian University of Science and Technology (NTNU, Trondheim, Norway). He is also the head of the chair Blériot-Fabre, sponsored by the group SAFRAN, at Centrale Supélec (Paris, France). During his career, he moved back and forth from academia to industry, being notably senior researcher at French National Center for Scientific Research (CNRS), associate professor at Universities of Bordeaux and Marseille, professor at Ecole Polytechnique and Ecole Centrale Paris, CEO of the start-up company ARBoost Technologies he founded, and director of the R&D department on Systems Engineering at Dassault Systemes (largest French software editor). He gothisPhDin1989 and his tenure (habilitation àdirigerdes Recherches) in 1996, both in computer science. He works in the reliability engineering field for more than 20 years. He extended his research topics to systems engineering more recently. He published over 150 articles in international conferences and journals. He is on the advisory boards of several international conferences and journals and is regularly invited to deliver keynote talks at international conferences. He renewed mathematical foundations and designed state-of-the-art algorithms for probabilistic safety/risk assessment. Alone or with his students and collaborators, he developed safety/risk assessment software that is daily used in industry (Aralia, XFTA, MarkXPR). He is also the main designer of the AltaRica modeling language and the scientific advisor of the Open-AltaRica project (IRT SystemX). He managed numerous collaborations between academia and industry, in Europe, in the USA and in Japan, and has been the adviser of fifteen PhD theses. |
02.03.2018 | Prof. Anne Barros | Title: On the Use of Piecewise Deterministic Markov Processes in Reliability and Maintenance PDMP_Anne Barros.pdf Abstract: Piecewise deterministic Markov processes (PDMP) are widely used in dynamic reliability to model phenomena which are considered as deterministic most of the time (e.g. evolution of the fluid level in a tank) and which are influenced from time to time by stochastic events (e.g. failures in the control loop for the fluid level). Usually, a PDMP is made of a set of differential equations (deterministic part) whose solutions can experience random “jumps” (effect of stochastic events). We present here a very specific type of PDMP: the deterministic part is reduced to a set of trivial differential equations whose solutions will be used to measure the time elapsed since the last stochastic events. Hence, the deterministic part is not related to any physical phenomena but is an artifact to model stochastic behaviors that require a combination of discrete random “jumps” and continuous variables to count time. We will discuss how such PDMP can be used to study maintained systems with several units. Stochastic jumps will model failure, repair, detection times while the continuous variables will model deterministic repair durations or delays, the time between inspections, time spent in different states of interest (especially in case of Weibull lifetime or repair time). We will try to explore how such a formalism can help for the modeling work, and when an explicit numerical scheme can be easily developed to calculate the quantities of interest (MDT, Availability, Reliability, Mean number of repairs...). About speaker: Anne Barros is a professor at NTNU in the Department of Mechanical and Industrial Engineering. Her research activity is focused on the use of stochastic processes in Reliability and Maintenance. Career: Master's degree in Systems Control from the University of Technology of Compiegne UTC (France), 2000, Master of Engineering in Industrial System from the University of Technology of Troyes UTT (France), 2000 and PhD (dr. philos) in Optimisation and System Safety from UTT, 2003. Associate Professor of Reliability and Maintenance engineering at UTT (2003-2011). Professor of Reliability and Maintenance engineering at UTT (2011-2014). Professor of Subsea Reliability at Norwegian University of Sciences and Technology NTNU (professorship founded by DNV-GL, 2014-). |
16.02.2018
| Harald Rødseth | Title: Risk-based maintenance backlog Maintenance backlog _Harald.pdf Abstract: TBAA relevant issue in manufacturing and production seems to be “silo”- organizations and “silo”-planning with lack of coordination between departments. Integrated Planning (IPL) is a concept that aims to cope with this “silo”-problem. With the ground-breaking potentials from Industry 4.0, it should be expected that the advancement of IPL will speed up in development and implementation in companies. To manage IPL sound key performance indicators (KPIs) must be implemented and established by the company. A promising indicator for IPL is maintenance backlog (MB). A strength of this indicator is the capability to be modeled with Risk OMT (Risk modeling – Integration of Organisational, human and Technical factors). It remains to investigate how MB can be modeled to a Quantitative Risk Analysis (QRA). The main objective of this article is to develop a model of MB in QRA. In particular, the article demonstrates a case study of a production system where both fault tree analysis (FTA), and event tree analysis (ETA) is modeled. The article discusses the demonstration results and evaluates how potentials in Industry 4.0 can support QRA. About speaker: Harald Rødseth, Postdoctoral fellow, MTP, NTNU. Opponents: Prof. Jørn Vatn & Ph.D. candidate Yun Zhang |
02.02.2018 | HyungJu Kim | Title: Systems-Theoretic Process Analysis (STPA) for subsea systemsSTPA to Subsea_HyungJun Kim.pdf Abstract: Systems-Theoretic Process Analysis (STPA) is a recently developed hazard identification technique that is based on control and systems theory. Previous studies on STPA emphasizes two major strengths of the method: (1) STPA provides a systematic top-down approach that enables early identification of system flaws, and (2) STPA covers a wider scope of hazards compared to traditional methods. Despite these advantages, there are only a limited number of studies that have applied the method to subsea systems. It is therefore of interest to investigate how STPA can be used to formulate new or verify existing requirements for safety-critical systems for subsea facilities. The contents of the presentation are 1) Introduction to STPA; 2) Ongoing STPA studies in RAMS group; 3) STPA to subsea gas compression system - ESREL 2018 conference; 4) STPAtoisolationofsubsea wells - OTC 2018 conference About speaker: Hyungju Kim is a postdoctoral fellow at RAMS group, MTP, NTNU. His research topic is new control and safety philosophies of subsea systems, which is a part of SFISUBPRO project. He completed his Ph.D. in RAMS at NTNU and earned his Master's and Bachelor's degree in Naval Architecture and Ocean Engineering at Seoul National University. Before he started his Ph.D. at NTNU, he worked for Samsung Heavy Industries for seven years, as a naval architect. |
| 26.01.2018 | Wenyan Song | Title: Data-driven fuzzy modeling method and its application Data driven_Wenyan.pdf Abstract: Fuzzy modeling method is an effective computation intelligence technology that can handle data information and human knowledge. Now it has been widely applied in system modeling, decision system design and data analysis. In this talk, we will introduce some basic concepts about fuzzy set theory and a few examples of fuzzy modeling method combined with other machine learning algorithms. Besides, we also introduce some application cases about fuzzy modeling. Speaker: Dr. Wenyan Song is an associated professor in School of Economic, Dongbei University of Finance and Economics University in China. She has research interests in machine-learning, and she works more in applying the technique to industrial process and financial market. |
19.01.2018 | Behnaz Hosseinnia Xiaopeng Li | Title: New Ph.D. Candidate Self-introduction Speaker: Behnaz Hosseinnia Self intorduction_Behnaz.pdf Abstract: Introduce herself and talk about her previous research project at Safety and Security Science group at TU Delft, the Netherlands. She will also give a brief introduction of her PhD research topic at RAMS group. About Speaker: New PhD student within RAMS group. She received her master degree in chemical engineering (minor: HSE) and did her undergraduate studies in Safety and Technical Protection Engineering in Petroleum University of Technology (Iran) with the first rank honor. In her M.Sc. thesis she has studied the application of fire and explosion modeling, failure probability analysis and quantitative risk-based decision making in petroleum pipelines emergency response planning. Besides the academic experience, two internships with National Iranian Oil Company (NIOC) and four-year work with engineering consultancies have enabled her to combine the technical knowledge with hands-on experience in risk analysis, process safety and accidents consequence modeling. From 2016, she worked as a researcher at the Safety and Security Science group in TU Delft, the Netherlands. Her research project was funded by LDE (Leiden. Delft. Erasmus) Centre for Safety and Security and focused on developing an effective decision-making tool for multi-plant emergency response planning against terrorist attacks in the chemical industrial areas. Speaker: Xiaopeng Li ( Exchange Ph.D. candidate) Self-introduction_Xiaopeng Li.pdf About Speaker: He is from College of Management and Economics, Tianjin University, China. He will introduce himself and his previous research works on warranty management in Tianjin University and his research plan in NTNU. |
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