Condition Monitoring and Predictive Maintenance (IAM540)

The course deals with condition monitoring and predictive maintenance of dynamic machinery and static mechanical equipment. It provides the project execution model to design and manage condition-based maintenance and predictive maintenance programs. The course provides the engineering analysis methods to analyse industrial equipment and structures, failure modes, and failure symptoms and determine suitable monitoring techniques (vibration, acoustic emission, ultrasonic, oil-debris, thermal and process parameters) and the required technical specifications.


Course description for study year 2024-2025. Please note that changes may occur.

Facts

Course code

IAM540

Version

1

Credits (ECTS)

10

Semester tution start

Autumn

Number of semesters

1

Exam semester

Autumn

Language of instruction

English

Content

The course compiles eight modules together as follows:

Module 1 is about condition-based maintenance and its standard ISO 17359.

Module 2 is about the most common industrial faults like imbalance, misalignment, bent shaft, bearing defects, gear faults, structural cracks in pipes and pressure vessels, and faults for on-demand equipment.

Module 3 is about monitoring techniques, i.e. vibration, acoustic emission, ultrasonic, oil-debris, thermal and process parameters.

Module 4 is about non-destructive testing (NDT) methods such as penetrant, flux leakage, eddy current, and radiography.

Module 5 is about a generic project execution model to design condition-monitored and predictive maintenance-ready equipment, and monitoring engineering methods: failure mode and symptom analysis, diagnostic coverage analysis, and predictive maintenance analysis.

Module 6 is about signal analysis, time and frequency domain detection and diagnostics analysis

Module 7 is about Model-based prognostics and Data-driven Prognostics.

Module 8 is about prescriptive maintenance and estimating remaining useful lifetime (RUL) for potential prescriptive scenarios.

Lectures, lab experiments, teamwork, oral presentation, project management, and communication with real-world stakeholders and the condition monitoring community, are all activities and skills embedded into the course modules to scaffold the learning performance. The learning is assessed and reinforced by several assignments, lab experiments, oral presentations and a course project.

Learning outcome

By completing this course, the students shall gain the following knowledge, skills and competencies:

Knowledge

  • Gain a comprehensive understanding of condition monitoring (CM), condition-based maintenance (CBM) and predictive maintenance (PdM).
  • Gain a comprehensive understanding of common machine faults: causes, mechanisms, symptoms, and modes.
  • Gain a basic understanding and theories behind the monitoring techniques, e.g. vibration, acoustic emission, ultrasonic, oil-debris, thermal and process parameters.
  • Gain a basic understanding and theories behind signal analysis (time and frequency domains), diagnosis and prognosis analysis.
  • Gain a basic understanding and theories behind the non-destructive testing (NDT) methods such as penetrant, flux leakage, eddy current, radiography.

Skills

  • Be able to apply the project execution model to design monitored and PdM-ready equipment and deliver Concept and front-end engineering (FEED) studies.
  • Be able to perform engineering analysis methods, e.g. Failure modes analysis, Symptoms Analysis, Sensor diagnostic coverage analysis, and PdM concept study.
  • Be able to perform time and frequency domain signal analysis.
  • Be able to perform diagnosis analysis and determine the fault type, location and severity level.
  • Be able to perform prognosis analysis (physics-based and/or data-driven) to predict the remaining useful lifetime.

General competence

  • can analyze relevant academic, professional, and research ethical problems
  • can work in teams and plan and manage projects.
  • can apply his/her knowledge and skills in new areas in order to carry out assignments and projects
  • can communicate about academic issues, analyses and conclusions in the field, both with specialists and the general public

Required prerequisite knowledge

None

Exam

Form of assessment Weight Duration Marks Aid
Folder 1/1 Letter grades All

The learning is assessed through four assignments: Concept assignment 20%, Lab assignment 20%, Course project assignment 30% and Reflection assignment 30%. All assignments are individualContinuation options are not offered. Students who do not pass can carry out a new assessment the next time the subject is taught.

Coursework requirements

Obligatory requirements
Laboratory exercises, company visits, and guest lectures.

Course teacher(s)

Course coordinator:

Idriss El-Thalji

Head of Department:

Mona Wetrhus Minde

Method of work

Lectures, assignment, laboratory exercises, company visits, guest lectures.

Overlapping courses

Course Reduction (SP)
Condition Monitoring and Predictive Maintenance (OFF540_1) 5

Open for

Admission to Single Courses at the Faculty of Science and Technology
Industrial Asset Management - Master of Science Degree Programme
Exchange programme at Faculty of Science and Technology

Course assessment

There must be an early dialogue between the course supervisor, the student union representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital subject evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.

Literature

The syllabus can be found in Leganto