...
NB: due to the corona situation, the seminars are also on Teams for now
Program 2021 Spring
| When | Who | What |
|---|---|---|
| 15.04.2021 | Postdoc Shenae Lee | More information to come. |
08.04.2021 | Ph.D.Candidate Lin Xie | The seminar on April 1st will be postponed due to Easter Holiday. More information to come. |
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 BSC in Engineering Physics from Indonesia and an MSc in RAMS from NTNU. He started his PhD candidate work at RAMS group, MTP, NTNU in August 2018 and will finalize his work this autumn 2021. The PhD 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 PhD 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. |
Program 2020 Autumn
| When | Who | What |
|---|---|---|
- | - | No more meetings until January due to the holiday season |
18.12.2020 | 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 | 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 | Ph.D. Candidate Ewa Maria Laskowska | Title: Predictive Maintenance |
| 06.11.2020 | 1 new Postdoc and 5 new PhDs | Title:New Ph.D. candidate and postdoctoral fellow self-introduction Xingheng Liu (postdoctoral fellow) |
...