Examination arrangement

Course content

The course is divided into the following 4 modules
 
1. Preventive Maintenance
-Terminology related to predictive maintenance
-Innovation processes within predictive maintenance
-Establish goals and strategies for predictive maintenance
-Management of predictive maintenance
 
2. Inspection and condition monitoring
  -Prerequisites for inspection and condition monitoring
-Organization of inspection and condition monitoring
-Selected methods for inspection and condition monitoring
  -Methodology for collecting condition data
-Methodology for analysis and washing of condition data
 
3. Diagnosis and prognosis using condition data
-Maticematical models
  -Machine learning
-Visualization of diagnosis and prognosis
 
4. Maintenance decisions
-Use of diagnosis and prognosis derived from condition data, in maintenance decisions

Learning outcome

Upon completing the course, the candidate should have the following basic knowledge of / skills in and general competence related to predictive maintenance:

Knowledge
The candidate has knowledge of:
- Collection of condition data on electrical and mechanical equipment
- Diagnostics of condition of electrical and mechanical equipment
- Use of condition data in the assessment of remaining useful life of electrical and mechanical equipment
- Maintenance decisions based on knowledge of condition and remaining useful life
 
In relation to ethics / innovation / digitization, the subject emphasizes developing the candidate's basis to reflect on:
Ethics
- How predictive maintenance can help improve resource utilization and optimum operational safety.
Innovation
- How knowledge about condition and remaining useful life time can be used to develop optimal maintenance of electrical and mechanical equipment.
Digitization
- Possibilities by using digital condition data in maintenance analyzes and decisions.

Skills
The candidate can:
- Participate in professional discussions on the use of predictive maintenance
- Choose a favorable method for diagnosing the condition of electrical and mechanical equipment
- Select methodology for collecting and evaluating condition data
- Use tools for assessment of degradation and remaining useful life of electrical and mechanical equipment
- Visualize their assessments of degradation and remaining useful life
- Use information on degradation and remaining useful life in maintenance decisions

In relation to ethics / innovation / digitization, the subject emphasizes developing the candidate's ability to:
Ethics:
- Communicate the consequences of using predictive maintenance.
Innovation:
- Discuss and suggest ways to optimize operation and maintenance with the use of predictive maintenance.
Digitization
- Collect, evaluate and use condition data in operation and maintenance analyzes and decisions.

General competence:
The candidate has the following skills:
- Practical understanding and insight into how predictive maintenance affects:
- reliability and service life
- health, environment and economics
- Practical understanding of and insight into the use of tools and methods used in predictive maintenance.
- Ability to formulate and discuss technical solutions in the field of predictive maintenance with skilled workers and senior personnel.

In relation to ethics / innovation / digitization, the subject emphasizes the following skills:
Ethics: The candidate must be able to
- Be critical and reflective of predictive maintenance.
- Demonstrate consistency with focus on lifetime thinking.
Innovation: Candidates must
- Know key innovation processes in predictive maintenance.
Digitization: Candidates must
- Knowing the implementation of digital tools in predictive maintenance.

Learning methods and activities

All lectures to be streamed. Recorded lectures to be placed on black board
Interactive lectures
- Guest lecturers from industry
- Use of videos
- Digital exercises
- Supervised excercises
- Laboratory work
- Project work / problem-based learning in groups

Compulsory assignments

• Excercise

Further on evaluation

Mandatory work from previous semester can be accepted by the Department by re-take of an examination if there have not been any significant changes later. If there is a re-sit examination, the examination form may be changed from written to oral.
Permitted examination aids:
All calculators are allowed.
Tekniske tabeller Redigert av Jarle Johannssen.
Formulas attached to the exam paper.

Specific conditions

Recommended previous knowledge

Basic knowledge of programming, python or the like.

Required previous knowledge

Admission to the course require admission to Bachelor studies in engineering at NTNU. Students can participate in the subject when agrred with subject responsible.

Course materials

Will be announced at the start of the course.