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| When | Who | What |
|---|---|---|
| 22.09.2022 | Ph.D. Candidate Muhammad Gibran Alfarizi | More information will come later. |
| 08.09.2022 | Postdoc Xingheng Liu More information will come later | 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 |
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