ERGO1300 Somatisk helse - aktivitet og deltakelse Emneplan

Emnet omfatter behandling og rehabilitering av personer med aktivitetsproblemer som følge av revmatologiske, nevrologiske, ortopediske eller indremedisinske sykdommer eller skader. Ergoterapeuter kan bidra til å trene opp tapt funksjon og/eller muliggjøre aktivitet og deltakelse. Her er aktivitetsanalyse og metodisk bruk av aktivitet viktige redskaper. Fokus er på funksjonshemming, rehabilitering og klientsentrering i et mangfoldig samfunn.

Emnet er sammensatt av følgende fagområder fra rammeplanen, angitt i studiepoeng:

• Idé, teori og erfaringsgrunnlag 2.
• Forskning og utvikling i ergoterapi 1.
• Rehabiliterende arbeid 2.
• Behandlende arbeid 3.
• Sykdom og funksjonshemming 7.

In today's fast-paced energy industry, predictive maintenance plays a crucial role in ensuring the reliability and efficiency of energy systems. This course is designed to equip students with advanced knowledge and skills in applying probabilistic machine learning techniques to optimize maintenance strategies for energy systems.

The course features a structured progression, starting with foundational concepts in systems engineering and energy systems, transitioning to maintenance engineering, and advancing to probabilistic machine learning techniques such as Gaussian processes, hidden Markov models, probabilistic graphical models, and deep belief networks. Real-world case studies provide hands-on experience, enabling doctoral students to bridge theoretical knowledge with practical applications in predictive maintenance.

Students who complete the course are expected to have the following learning outcomes, defined in terms of knowledge, skills and general competence:

Knowledge:

• Understand the principles of systems engineering and their relevance to energy systems and predictive maintenance.
• Gain a comprehensive overview of energy systems, from fossil fuels to renewables, and the unique challenges they present.
• Acquire advanced knowledge of probabilistic machine learning techniques, including Gaussian processes, hidden Markov models, probabilistic graphical models, and deep belief networks.
• Understand the integration of machine learning techniques into predictive maintenance frameworks and their impact on system reliability.

Skills:

• Apply systems engineering principles to design and analyze energy systems, ensuring efficient integration of predictive maintenance strategies.
• Develop and implement predictive maintenance frameworks tailored to energy systems, transitioning from reactive to proactive maintenance approaches.
• Employ probabilistic machine learning techniques, such as Gaussian processes and graphical models, to model system behavior, predict failures, and optimize performance.

Competence:

• Collaborate effectively across disciplines to design solutions for predictive maintenance in diverse energy system contexts.
• Evaluate uncertainties in predictions and make informed decisions to improve the reliability and efficiency of energy systems through predictive maintenance strategies.
• Demonstrate proficiency in implementing probabilistic machine learning algorithms using Python and relevant libraries for energy system predictive maintenance tasks.

Teaching methods will include lectures, group work and guest lectures from industry personnel.

For å fremstille seg til eksamen må kravene til obligatorisk tilstedeværelse være tilfredsstilt.

Individuell muntlig eksamen, inntil 30 minutter.

Tidspunkt: 2. semester.

Ingen hjelpemidler

Gradert skala A-F.

Ekstern og intern sensor vurderer minimum 20 prosent av alle studentene. To interne sensorer vurderer de øvrige. Ekstern sensors vurdering skal komme alle studentene til gode.