0
Jump to main content

Condition monitoring of fibre ropes using machine learning

Shaun Falconer of the Faculty of Engineering and Science at the University of Agder has submitted his thesis entitled «Condition monitoring of fibre ropes using machine learning» and will defend the thesis for the PhD-degree Tuesday 28 June 2022. (Photo: Private)

The expected outcome is to use physics-based machine learning methods to improve both condition classification and remaining useful life estimation of fibre ropes used in offshore lifting operations.

Shaun Falconer

PhD Candidate

You may follow the disputation on campus or online. Link for registration as an online spectator at the bottom of this page.

 

Shaun Falconer of the Faculty of Engineering and Science at the University of Agder has submitted his thesis entitled «Condition monitoring of fibre ropes using machine learning» and will defend the thesis for the PhD-degree Tuesday 28 June 2022. 

He has followed the PhD-programme at the Faculty of Engineering and Science at the University of Agder with specialisation in Engineering Sciences, Scientific field Mechatronics. The doctoral work are funded by the Norwegian Research Council through UiA SFI Offshore Mechatronics.

Summary of the thesis by Shaun Falconer:

Condition monitoring of fibre ropes using machine learning

The application of fibre ropes in offshore lifting operations has significant potential for further development.

With minimum breaking loads (MBL) equivalent to steel wire at similar diameters and almost neutral buoyancy in water, it is in theory possible to reach depths exceeding 3000 m with smaller cranes and vessels, representing substantial savings in not only potential operation costs.

New standards needed

However, with fibre ropes there are different requirements and standards to consider with regards to condition monitoring, maintenance and retirement criteria.

Safe and reliable operations are paramount in the offshore sector and any incidents that occur during offshore lifting would not be only significantly damaging financially but could potentially lead to loss of life.

Current standards for fibre rope condition monitoring originate in mooring applications, based on manual inspection for retirement and re-certification.

There is significant room for developments in methods that can aid the inspection process.

New methods developed at MIL

To address this problem, computer vision and thermal monitoring methods for fibre ropes are developed and experimentally investigated at the Mechatronics Innovation Lab (MIL) in Grimstad, Norway.

The methods are used to monitor changes in fibre rope condition during cyclic-bend-over-sheave testing and to find relevant condition indicators that give more information regarding the condition and remaining useful life of the fibre rope.

In addition, the data recorded is used to form machine learning models that both classify rope condition and predict the remaining life of fibre ropes during CBOS testing.

The expected outcome is to use physics-based machine learning methods to improve both condition classification and remaining useful life estimation of fibre ropes used in offshore lifting operations.

In the appended papers at the end of this thesis, the proposed methods have been experimentally investigated and validated through cyclic-bend-over-sheave experiments performed at the Mechatronics Innovation Lab and further data analysis performed at the University of Agder, Norway and at divis in Dortmund, Germany.

Disputation facts

The trial lecture and the public defence will take place on campus in Auditorium C2 040, Campus Grimstad, and online (registration link below) via the Zoom conferencing app - registration link below.

Dean Michael Rygaard Hansen, Faculty of Engineering and Science, University of Agder, will chair the disputation.

The trial lecture Tuesday 28 June at 10:15 hours

Public defense Tuesday 28 June at 12:15 hours

  

Given topic for trial lecture«Practical means to extend the remaining work life of ropes for lifting and mooring applications»

Thesis Title«Condition monitoring of fibre ropes using machine learning»

Search for the thesis in AURA - Agder University Research Archive, a digital archive of scientific papers, theses and dissertations from the academic staff and students at the University of Agder.

The thesis is available here:

https://uia.brage.unit.no/uia-xmlui/handle/11250/2998488

 

The CandidateShaun Falconer (1991, Glasgow, Scotland) MEng Mechanical Engineering with Aeronautics, University of Glasgow, Scotland (2014). Present position: Pipe System Design Engineer at NOV Flexibles, Denmark.

Opponents:

First opponent: Professor Vilmar Æsøy, Department of Ocean Operations and Civil Engineering, NTNU - Norwegian University of Science and Technology

Second opponent: Professor Robert Schulz, Institute of Mechanical Handling and Logistics (IFT), University of Stuttgart, Germany

Associate Professor Dmitry Vysochinskiy, Department of Engineering Sciences, University of Agder,  is appointed as the administrator for the assessment committee.

Supervisors in the doctoral work were Professor Geir Grasmo, University of Agder (main supervisor) and Senior Researcher Ellen Nordgård-HansenNORCE (co-supervisor)

What to do as an online audience member:

The disputation is open to the public, but to follow the trial lecture and the public defence online, transmitted via the Zoom conferencing app, you have to register as an audience member on this link:

https://uiano.zoom.us/meeting/register/u5Itdeysqj0rEtWTszc24AVQuzRYimtD7WKq

A Zoom-link will be returned to you. (Here are introductions for how to use Zoom: support.zoom.us if you cannot join by clicking on the link.)

We ask online audience members to join the virtual trial lecture at 10:05 at the earliest and the public defense at 12:05 at the earliest. After these times, you can leave and rejoin the meeting at any time. Further, we ask audience members to turn off their microphone and camera and keep them turned off throughout the event. You do this at the bottom left of the image when in Zoom. We recommend you use ‘Speaker view’. You select that at the top right corner of the video window when in Zoom.

Opponent ex auditorio:

The chair invites members of the public to pose questions ex auditorio in the introduction to the public defense. Deadline is during the break between the two opponents. The person asking questions should have read the thesis. For online audience the Contact Persons e-mail are available in the chat function during the Public Defense, and questions ex auditorio can be submitted to Emma Elisabeth Horneman on e-mail emma.e.horneman@uia.no