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Program 2022 Autumn
| When | Who | What |
|---|---|---|
| 03.11.2022 | Ph.D. Candidate Alessandro Campari | More information will come later. |
27.10.2022 15:00-16:00 (Digital Only) | Chi Ji | Title: Autonomy safety and SOTIF Abstract: The presentation will start from the introduction of key technologies in autonomous driving and challenges in autonomy safety. SOTIF (safety and intended functionality) will be then presented, including the approach of SOTIF analysis, acceptance criteria and SOTIF validation targets, followed with examples of calculation methods. About Speaker: Mrs. Ji has master’s degrees in both engineering and business administration. She has 12 years of working experience in automobile industry (including airbag, braking and autonomous driving). From 2010 to 2018, Mrs. Ji worked at Autoliv and ZF, she was in charge of the functional safety aspects of airbag and braking systems. After 8 years’ in Tier1, Mrs. Ji joined the SAIC Autonomous Driving Center, her main job was focused on L2+ autonomous driving, as well as system level safety analysis work. Last year she joined UL and become an Autonomy safety consultant. |
05.10.2022 | Ph.D. Candidate Michael Pacevicius | There will be no seminar this week. Instead, you are invited to Michael Pacevicius’s defense.
More information can be found here. |
| 22.09.2022 | Ph.D. Candidate Muhammad Gibran Alfarizi | Abstract: Bearings are essential to the reliable operation of rotating machinery in manufacturing processes. There is a rising demand for accurate bearing remaining useful life (RUL) predictions. The data-driven technique for predicting RUL of bearing has demonstrated promising prospects to facilitate intelligent prognostics. This paper proposes a new data-driven prediction framework for bearing RUL utilizing an integration of empirical mode decomposition, random forest, and Bayesian optimization. The proposed framework consists of two main phases: feature extraction and RUL prediction. The first phase of this framework focused on decomposing the empirical input signals using empirical mode decomposition into distinct frequency bands to filter out irrelevant frequencies and determine the fault characteristics of the signals. In the second phase, the RUL prediction is then carried out by an RFs-based model with its hyperparameters tuned by Bayesian optimization. The proposed approach is validated using datasets obtained from an actual run-to-failure experiment of roller bearings. The experiment results significantly improved compared to the standard data-driven and stochastic approaches. (The paper can be found here). Hydrogen can be adopted as a clean alternative to hydrocarbons fuels in the marine sector. Liquid hydrogen (LH2) is an efficient solution to transport and store hydrogen onboard of large ships. LH2 will be implemented in the maritime field in the near future. Additional safety knowledge is required since this is a new application and emerging risk might arise. Recently, a series of LH2 large-scale release tests was carried out in an outdoor facility as well as in a closed room to simulate spills during a bunkering procedure and inside the ship’s tank connection space, respectively. The extremely low boiling point of hydrogen (-253°C) can cause condensation or even solidification of oxygen and nitrogen contained in air, and thus enrich with oxygen the flammable mixture. This can represent a safety concern since it was demonstrated that a burning mixture of LH2 and solid oxygen may transition to detonation. In this study, the experimental data of an LH2 release test series recently carried out were analysed by means of an advanced machine learning approach. The aim of this study was to provide critical insights on the oxygen condensation and solidification during an LH2 accidental release. In particular, a model was developed to predict the possibility and the location of the oxygen phase change depending on the operative conditions during the bunkering operation (e.g. LH2 flowrate). The model demonstrated accurate and reliable predicting capabilities. The outcomes of the model can be exploited to select effective safety barriers such as a water deluge system to prevent the oxygen change phase. 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. |
| 08.09.2022 | Postdoc Xingheng Liu | Title: Spatial-temporal interpolation for condition monitoring: an application to choke valves Abstract: Critical systems such as subsea choke valves works under a time-varying operating condition (TVOC), which makes it challenging to estimate the system’s health state since the health indicator (HI) may be recorded under different TVOC. State-space models together with Particle Filter are widely used for system identification when the functional forms of the relationship between TVOC and HI are explicitly given. However, assumptions on how the TVOC can influence the observation and the growth of HI are generally hard to validate in practice. In this presentation, we introduce the spatial-temporal interpolation methods for state estimation and prognosis for subsea choke valves. Spatio-temporal interpolation is the task of estimating the unknown values of some property at arbitrary spatial locations and times, using the known values at spatial locations and times where measurements were made. The estimated property varies with both space and time, with the assumption that the values are closer to each other with decreasing spatial and temporal distances. For subsea choke valves, the HI (flow coefficient deviation) is the quantity to be interpolated, the operating condition (percent travel) constitutes the 1-D space dimension and the timestamps at which the observations were made form the time dimension. Two popular methods, namely spatial-temporal Inverse Distance Weighting and Universal Kriging, are fitted to the data, before being compared to some other competing models (ARIMA, Wiener process…) We highlight the difference between the fundamental assumptions in these models and showcase the pros and cons of each model in terms of forecasting. Keywords: condition monitoring, state estimation, spatial-temporal interpolation, Kriging, Forecasting. |
25.08.2022 10:00-11:00 | Ph.D. Candidate Tom Ivar Pedersen | Title: Industry 4.0 and Smart Maintenance Abstract: In this RAMS seminar, I will present the results of my Ph.D. project, which is soon to be completed. My Ph.D. project belongs to the research program BRU21. BRU21 stands for Better Resource Utilization in the 21st century and is NTNU’s research and innovation program in digital and automation solutions for the Norwegian oil and gas industry. The main objectives of my Ph.D. project have been to explore how the introduction of digital solutions and concepts from Industry 4.0 to maintenance can help improve the competitiveness of this industry sector. Opponents: Postdoctoral fellow Xingheng Liu, PhD Candidate Endre Sølvsberg |
Program 2022 Spring
| When | Who | What |
|---|---|---|
| - | - | No more meetings until August due to the holiday season |
09.06.2022 | Guest Ph.D. Candidate Théo Serru | Title: Modeling Cyberattacks Propagation in Cyber-Physical Systems using Discrete Event Simulation Abstract: Cyber-Physical Systems (CPS) are more and more used in our everyday life. Such systems are safety critical as they can have catastrophic effects on their environment and users. To lower the risks of safety critical situation, model-based safety assessments (MBSA) of such systems have been developed and used in industry. However, the very nature of CPS makes them vulnerable not only to accidental failure but also to cyberattacks. Thus, this seminar will present an approach based on discrete event systems to analyze the effects of multi-step cyberattacks on the safety of CPS. We show how to represent systems, their components (either software and/or hardware), links, security measures, and attacks from a malicious intruder. We then show how the formal modeling language AltaRica, primarily dedicated to safety analyses, can be used to assess cyberattacks by representing the system and extracting automatically sequences of attacks leading to a safety critical situation. Finally, as the extraction of sequences is subject to state-space explosion, we will introduce a new notion called footprint. This cutoff allows to consider the dependent nature of cyberattacks to lower the state-space and reduce the number of sequences generated to keep the more likely (without using probabilities). About Speaker: Théo Serru is a PhD candidate in a thesis funded by CY initiative excellence and Apsys-Airbus. The subject of his research is the formal modeling of safety and cybersecurity properties on cyber-physical systems. He is also a dependability engineer graduated from the engineering school Polytech Angers. |
| 19.05.2022 | Ph.D. Candidate Yixin Zhao | Title: An Extended Cascading Failure Model for Loading Dependent Systems with Multi-state Components Abstract: Many production systems consisting of multiple components are vulnerable to the cascading failures, and one example is that the overloading of a component will also affect the other components. Such loading dependence can result in failure propagation and make the systems unavailable and the maintenances more challenging. In this study, we develop a multistate CASCADE model for evaluating the propagation process of cascading failures in loading dependent systems. The multinomial distribution is applied and derived to represent the probability of total number of overloading components and failures. Probability distributions of cascading process stop scenarios are also developed. Numerical examples are investigated with the proposed model for evaluating the factors influencing the probability distributions of total number of failed and overloading components, as well as the occurrences of three stop scenarios. The multi-state CASCADE model and numerical study can provide reference for optimization of some controllable variables in design or maintenance of a general loading dependent system subjected to cascading failures. 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. |
| 05.05.2022 | Ph.D. Candidate Wanwan Zhang | Title: Condition-based opportunistic maintenance of cascaded hydropower stations Abstract: The purpose of this paper is to build a new condition-based opportunistic maintenance (CBOM) model. It combines short-term hydro scheduling (STHS) and generator maintenance scheduling (GMS) by failure property. One generator in a cascaded hydro system is used as research example. CBOM model schedules 9 maintenance activities in one year for this generator. Sensitivity analysis reveals that this model offers sufficient flexibility to modify scheduling plans based on maintenance requirements. In all the parameters, accident penalty cost and maintenance duration have no effect on maintenance results. Upper and lower limits of failure probability influence the number of maintenance activities. Compared with age-based maintenance (ABM), CBOM strategy obtains more profits and cancels unnecessary maintenance activities by trade-off between operation and maintenance. |
28.04.2022 (12:00-13:00) | Postdoctoral Fellow Federico Ustolin | Title: Modelling of accident scenarios from hydrogen transport and use Abstract: Hydrogen is one of the best candidates in replacing fossil fuels in order to tackle global warming and decarbonise the energy sector. Therefore, hydrogen could be employed in new applications (viz. road transport, maritime, aviation) from which emerging risks might arise. Knowledge gaps for many phenomena still exist for hydrogen. In particular, the behaviour of cryogenic liquid hydrogen during some accident scenarios (e.g. physical explosions) is still unknown. Thus, different research questions emerge: how a cryogenic liquid hydrogen tank performs when exposed to a fire? And what are the consequences of its catastrophic rupture? Are the risk-based inspection methodologies effective for hydrogen technologies? The answers to these questions and an overview on hydrogen safety research carried out at NTNU by the RAMS group will be provided during the seminar. About speaker: Federico Ustolin graduated in Mechanical Engineering at the University of Trieste, Italy. In July 2021, he was awarded a PhD in Mechanical Engineering by NTNU. Since 2018 Federico has been part of the RAMS group at NTNU, he is currently Postdoctoral fellow at MTP, and his research focuses on hydrogen safety. In November 2021, he got the Hydrogen Europe Research Young Scientist Award 2021 for the cross-cutting pillar. |
| 07.04.2022 | Ph.D. Candidate Emefon Dan | Title: Performance assessment of redundant strategies for multi-component system subject to random shocks Abstract: Redundancy is often essential for achieving high system availability. An additional benefit of installing redundant components is that the total system load can be shared among the components. While the system may benefit from having more redundant components, the active components often share a common source of random failure (shocks) which may lead to unexpected system downtime. In this study, we analyse the performance of two redundant strategies: active strategy, where all the components are running from the start and benefit from load sharing but are exposed to a common source of random shocks and a passive redundancy strategy where one of the components is in standby raising the workload of the active components on the one hand, but unaffected by the common source of shocks on the other hand. We compare the performance of both strategies for different frequencies of occurrence of the random shocks and different degrees of load sharing. About Speaker: Emefon Dan has a bachelors in Mechanical Engineering from Nigeria and an MSc in RAMS from NTNU. He started his PhD in October 2021. He is working on Condition-based maintenance decisions for subsea systems with digital twins. |
| 24.03.2022 | Ph.D. Candidate Bahareh Tajiani | Title: Lead Time Modeling for Optimization of an Alarm Threshold Abstract: Lead time is the time from when a spare part is ordered until it arrives, or it is the time from when a maintenance action is ordered until it is carried out. Most recent literatures focused on deterministic lead time in maintenance context to find an optimal maintenance policy, however in real-life applications, lead time is a stochastic variable depending on many factors such as availability of the maintenance team, type of failure, delivery time of an item, etc. In this presentation, we will discuss the deterministic and stochastic lead time modeling for the continuously monitored systems subject to gradual degradation in order to find an optimal alarm threshold. Furthermore, some ideas will be proposed regarding how the model can be improved to consider different failure mechanisms such as external shocks. 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. |
| 10.03.2022 | Ph.D. Candidate Jie Liu | Abstract: The topic of the presentation is about comparison of RUL prediction models which including stochastic approaches and digital twin of Matlab. Data used for prediction is from experiment of RAMS lab. Two stochastic approaches are selected which are Wiener process and Geometric Brownian Motion. The purpose of the study is to compare the models for remaining useful life prediction with standard stochastic approaches and digital twin through real degradation data and try to find the comparison among them. The research could be used as a reference for further remaining useful life prediction research. About Speaker: Jie Liu has a bachelor’s degree in Applied Mathematics in Beijing Institute of Technology from China and a master’s degree in RAMS of NTNU. She started as a PhD candidate at RAMS group, MTP, NTNU in September 2021. Her research topic is Digital Twin Qualification for Maintenance, which is a part of SUBPRO project. |
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. |
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