Disputas - Master of Science Rezgar Zaki

Master of Science Rezgar Zaki will Friday December 4th at 12:15 PM publically defend his thesis for the PhD degree in Science.

Title of the thesis:

"Performance Measurement System in Complex Environment: Observed and Unobserved Risk Factors"

 

Popular scientific abstract:

"World demand for energy leads industry to harvest energy in complex environment with harsh conditions and sensitive areas, such as the Arctic region – one of the last remaining wild places in the world – with potentially harmful consequences. Moreover, over the past few decades, the increasing trend of melting sea ice in the Arctic has provided increased access and has created new opportunities for economic development within metals and minerals, fisheries, cargo shipping, cruising, subsea telecom cables and pipelines. However, development of the Arctic resources is assumed to be technologically and economically challenging and risky. Studies reveal that, due to low temperatures, sea ice, polar low pressures, poor visibility, seasonal darkness limitations to the logistics of supplies, etc., Arctic operational conditions have significant effects on the performance of components and industry activities in various ways, including increasing failure rate and repair time, and can cause different types of production losses. The optimal functioning of technical systems involved in design and operation in the Arctic faces numerous challenges, in order to succeed in a globally competitive market with limited resources. The concept of the Performance Measurement System (PMS) is frequently used by industries and has been shown to be an essential concept for improving efficiency and effectiveness and supporting the design, planning, and managing of a company; PMS refers to output results obtained from a system that permits evaluation and comparison, relative to past results or other companies. PMS needs up-to-date and accurate performance information on its business. This performance information needs to be integrated, dynamic and accessible, to assist fast decision-making. However, performance terminologies and standards for the Arctic reveal that the Performance Indicators (PIs) measured by industries though important, are not enough and could still be improved by identifying more important indicators, which contribute to a successful PMS in the Arctic. Hence, the development and continuous improvement of PMSs and the identification of more PIs for judging performance of equipment in the Arctic are critical for industry success. Moreover, the quantification of performance is complex, as it involves various indicators with different perspectives at various hierarchical levels. The lack of correct sources of information and data on PIs and suitable statistical models and standard approaches are a barrier to the successful quantification of PIs. Operation and maintenance data are often collected from multiple and distributed units in different operational conditions, which can introduce heterogeneity into the data. Part of such heterogeneity can be explained by the observable risk factors, whose values and the way that they can affect the item’s PIs are known. However, some factors which may affect PIs are typically unknown (unobserved risk factors), leading to unobserved heterogeneity. Nevertheless, many researchers have ignored the effect of observed and un-observed risk factors, and this may lead to erroneous model selection, as well as wrong conclusions and decisions. The statistics models must be able to quantify the effect of observed and unobserved risk factors on PIs and must be built based on correct assumptions that reflect the operational conditions. In this thesis, a methodology for the monitoring and analysis of operation and maintenance performance is developed. The aim is to facilitate improvements and the optimization of decision-making for operation and maintenance in the Arctic. Firstly, a brief survey of technological and operational challenges in the Arctic region, from a performance point of view, is presented. Further, appropriate performance indicators/criteria that need to be measured for judging the performance of equipment/systems in the Arctic that contribute to a successful PMS will be discussed. Thereafter, the study focuses on improvement and modifying the available statistical approach for the prediction of PIs, considering the effect of observed and unobserved risk factors."

The thesis is published in Munin and is available at: https://hdl.handle.net/10037/19902

 

Supervisors:

  • Professor Abbas Barabadi, Department of Technology and Safety, UiT (main supervisor)
  • Associate Professor Yuan Fuqing, Department of Technology and Safety, UiT
  • Post.doc. Amir Garmabaki, Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology
  • Associate Professor Sigrunn Holbek Sørbye, Department of Mathematics and Statistics, UiT

 

 

Evaluation committee:

  • Professor R. M. Chandima Ratnayake,Department of Mechanical and Structural Engineering and Materials Science, University of Stavanger (1. opponent)
  • Professor Alireza Ahmadi, Division of Operation and Maintenance Engineering, Luleå University of Technology, Sverige (2. opponent)
  • Professor Jinmei Lu, Department of Technology and Safety, UiT (internal member and leader of the committee)

Reserve member: Associate Professor Bjørn-Morten Batalden, Department of Technology and Safety, UiT

Both opponents will participate remotely to the defence.

 

Leader of the public defense:
The leader of the public defense is Professor Camilla Brekke, Vice-Dean Research, Faculty of Science and Technology, UiT.

 

Opposition ex auditorio:
If you have any questions for the candidate during the public defence, please send an e-mail to the leader of the public defence. They will announce the questions during the defence.

 

Trial lecture:

The trial lecture is held Friday December 4th at 10:15 AM in the same auditorium.

Title of the trial lecture: «Use of probabilistic and statistical approaches supported by machine learning for optimal decisions in dependability assessment: Asset operations in harsh environments»

 

Streaming:

The defense and trial lecture will be streamed via Mediasite: https://mediasite.uit.no/Mediasite/Catalog/Full/9c3ebb35e62e4805b33923e6a027e08c21

 

Audience:

UiT follows the national guidelines regarding infection control. A maximum of 20 people are allowed in the auditorium during the defence, as long as everybody keeps a distance of 1 meter at all times.

When: 04.12.20 at 12.15–15.00
Where: Auditorium 1.022, Teknologibygget
Location / Campus: Digital, Tromsø
Target group: Employees, Students, Guests, Invited
Contact: Jakob Holden Hansen
E-mail: jakob.h.hansen@uit.no
Add to calendar