| Week | Date | Subject
area | Lectured topics | Motivation | Lecturer | Tutorials |
|---|
| 35 | 25. & 26.8 | | 1st hour: - Introduction to the course
- Organization of student groups
(3 persons per group)
2nd & -3rd hour: - Introduction to two case studies
- Group work and summary in
plenum
| Inform the students about the course objectives, intended learning outcomes, and practicalities. - Give a more thorough introduction to two systems where the lectured models and methods
may be applicable. - Explain and discuss the technologies involved, with focus on attributes like
reliability, availability, maintenance, and safety
| Mary Ann | |
| 36 | 2.-3.9 | 2 | Age, block, and minimal repair
strategies | Maintenance optimization:
The intervals of maintenance for safety-critical systems are normally determined from the reliability analyses.
For other systems, we may use decide upon intervals of testing using different maintenance strategies
established by the RCM decision logic. These models include parameters like costs, failure rates, and aging.
The models come of with the answer to the following two questions: When should we do maintenance and
what tasks and equipment should be included. The application of these methods are many. Two examples are
maintenance planning of railway tracks and another is planning of intervention (for maintenance purposes) of
subsea equipment. Sub-topics also covered under the same "umbrella" are: - Modeling of effective failure rate: Maintenance interval and and intervention level (extensiveness of
maintenance) is obviously influencing the failure rate of the components. This topic concerns the modeling of the
relationship between these two parameters and what we can refer to as the effective (or resulting) failure rate. - Weibull renewal: **Say something here**
- PF models and state based models: **Say something here**
| Jørn | |
| 37 | 9.-10.9 | 2 | Age, block, and minimal repair
strategies (continued) | | | |
| 38 | 16.-17.9 | 2 | Spare-part optimization | | | | | Spare parts may be costly to have on the stock, but at the same time it is costly not to have a spare part available
when it is needed. This topic concern how to calculate the probability of running out of spares, using simple formulas
and Markov analyses. The use of PetriNets for this purpose is also shown. This topic may not be some relevant for very
specialized systems, where it is not possible to aquire a spare within short time. For a manufacturer that develops
products, such as sensors, in a large scale to e.g. the oil and gas industry, it may be relevant to find the optimal number of spare parts for warranty and repair services. | Yiliu (Mary Ann
at the ESREL
conference) | |
| 39 | | | | | | |
| 40 | | | | | | |
| 41 | | | | | | |
| 42 | | | | | | |
| 43 | | | | | | |
| 44 | 27&28.10 | | | | | |
| 45 | 4&5.11 | | | | | |
| 46 | 11&12.11 | | | | | |
| 47 | 18.&19.11 | N/A | Student presentations
(also using tutorial hours) | Students get the possibility to reflect on the lectured
topics and in particular to see how these are related to
their specialization project, and how they may be
applicable for their master project. | | |
| 48 | | | Summary | | | |
| | | | | | | |
| | | | | | | |
