course-details-portlet

MAST2012 - Condition Based Maintenance

About

Examination arrangement

Examination arrangement: Aggregate score
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
Group assignment 40/100
School exam 60/100 3 hours HJELPEMIDD

Course content

The course is taught and supervised in Norwegian.

The focus of the course is the collection of condition data and the use of such data in analyzes that form the basis for operation and maintenance decisions.

The course is divided into the following 4 modules

1. Predictive 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

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

The candidate has knowledge of: Collection of condition data on technical installations. Diagnosis of condition of technical installations Use of condition data in assessment of residual life Maintenance decisions based on knowledge of condition and remaining life How condition based maintenance can contribute to better resource utilization and optimal operational security. How knowledge of condition and remaining life can be used in the development of optimal maintenance Opportunities in using digital condition data in maintenance analyzes and decisions.

Skills: The candidate can: Participate in professional discussions about the use of condition based maintenance Choose favorable method for diagnosing the condition of technical installations Choose a methodology for collecting and assessing condition data Use tools for assessing degradation and residual life of electrical and mechanical equipment Visualize their assessments of degradation and longevity Use information about degradation and residual life in maintenance decisions Communicate consequences associated with using predictive maintenance.

General competence: The candidate has the following competence: Practical understanding and insight into how condition based maintenance affects: - reliability and longevity - health, environment and economy Practical understanding of and insight into the use of tools and methods used in condition based maintenance. Ability to formulate and discuss technical solutions in condition based maintenance with skilled workers and leading personnel on a professional basis. Be critical and reflective of condition based maintenance. Demonstrate an understanding of consequences with a focus on lifelong thinking. The candidates are familiar with key innovation processes within condition based maintenance The candidates are familiar with the implementation of digital tools in condition based maintenance.

Learning methods and activities

In addition to teaching in the physical space, all lectures to be streamed. Recorded lectures to be placed on NTNU'S LMS system.

  • Interactive lectures
  • Guest lecturers from the industry (internet based)
  • Use of videos
  • Digital exercises in blackboard
  • Supervised excercises
  • Laboratory excercise
  • Project work / problem based learning in groups

Mandatory work requirements:

Quizzes, blogs and lab reports to be submitted in the LMS system.

A certain number of approved quizzes, blogs and laboratory exercises, stated at semester start, are mandatory work requirements.

Compulsory assignments

  • Lab Report
  • Quizz in BlackBoard
  • Blogg in BlackBoard

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.

The assessment (A-F) in this course is based on :

  • a written "school exam" Inspera which counts for 60% of the total grade
  • and a group assignment submitted in Inspera which counts for 40% of the total grade.
  • Students must pass both the "school exam" and the group assignment.

Allowed exam aids: Tekniske tabeller av Jarle Johannessen, aids distributed at the exam.

Re-sit examination in August. If there is a re-sit examination, the examination form may be changed from written to oral.

Continuation and voluntary repetition / improvement can be carried out for some sub-evaluations without having to reassess both sub-evaluations in a topic (on the condition that the subject has not changed the assessment system). There is an opportunity to complain about partial assessments in this topic before all sub-evaluations have been completed.

Specific conditions

Admission to a programme of study is required:
Aquaculture - Engineering (BIHAV)
Mechanical Engineering (BIMASKIN)

Course materials

Will be announced at the start of the course.

Online resources found in the NTNU library and the National Library are used in the course.

The book - An introduction to predictive maintenance av R. Keith Mobley (1943-) is reccomended. Found in the digital format at the NTNU library

More on the course

No

Facts

Version: 1
Credits:  7.5 SP
Study level: Intermediate course, level II

Coursework

Term no.: 1
Teaching semester:  SPRING 2024

Language of instruction: Norwegian

Location: Trondheim

Subject area(s)
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
Department of Mechanical and Industrial Engineering

Examination

Examination arrangement: Aggregate score

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Spring ORD School exam 60/100 HJELPEMIDD 2024-05-06 09:00 INSPERA
Room Building Number of candidates
SL310 hvit sone Sluppenvegen 14 29
Spring ORD Group assignment 40/100

Release
2024-04-16

Submission
2024-04-23


10:00


14:00

INSPERA
Room Building Number of candidates
Summer UTS School exam 60/100 HJELPEMIDD INSPERA
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
Examination

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

More on examinations at NTNU