Model Volvo Truck Lifetime Repair History - Revision history

Slawek at 15:03, 18 October 2016

2016-10-18T15:03:55Z

Slawek at 00:31, 22 February 2016

2016-02-22T00:31:46Z

Slawek at 11:56, 14 October 2015

2015-10-14T11:56:44Z

Slawek at 11:55, 12 January 2015

2015-01-12T11:55:28Z

Slawek at 08:59, 23 October 2014

2014-10-23T08:59:11Z

10.0.2.2: Created page with "{{StudentProjectTemplate |Summary=Finding good representations for data-driven description of Volvo truck's repair and maintenance history |Keywords=Data Mining, Knowledge Rep..."

2014-03-07T11:46:46Z

Created page with "{{StudentProjectTemplate |Summary=Finding good representations for data-driven description of Volvo truck's repair and maintenance history |Keywords=Data Mining, Knowledge Rep..."

New page

{{StudentProjectTemplate
|Summary=Finding good representations for data-driven description of Volvo truck's repair and maintenance history
|Keywords=Data Mining, Knowledge Representation
|TimeFrame=Spring 2014
|References=http://www.sciencedirect.com/science/article/pii/S000437029800023X

http://www.pomdp.org/index.shtml

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.129.7714

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.1619

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.335.8737
|Prerequisites=Programming competence
Cooperating Intelligent Systems and Learning Systems courses

|Supervisor=Sławomir Nowaczyk, Rune Prytz
|Level=Master
|Status=Open
}}
This project aims at analysing historical records of workshop visits, detailing repair and maintenance work, for 70.000 Volvo trucks over a period of more than 10 years. The idea is to use data mining and machine learning techniques in order to find structure and regularities in this data. From both scientific and practical point of view, it is interesting to provide classification (either as a taxonomy or an ontology) of different service events.

We have access to the data containing different vehicles, different faults and different components. However, this data is represented in a complex, structured form. In order to make it easier to use, it is necessary to analyse this data and extract useful information from it, both relatively simple one (e.g. what are the most common faults and/or long term driving cost factors), as well as more complex and abstract one (e.g. it is important to distinguish between repair and maintenance actions, as well as between operations affecting different subsystems of the truck).

Moreover, by analysing the frequency and cost of different repairs, together with finding relations and dependencies between various classes of them, one can expect to significantly decrease cost and increase effectiveness of maintenance and diagnosis.

In particular, we are interested in modelling the interdependencies between different faults and repairs. The goal is to create a model of truck's lifetime maintenance needs, which would at any point in time give accurate estimates of what are the most likely problems this vehicle may encounter. Possible approaches to investigate include Partially Observable Markov Decision Process for modelling component state of health/wear, or Bayesian Networks for capturing relationships between various faults.

@@ Line 14: / Line 14: @@
 |Prerequisites=Programming competence
 Artificial Intelligence, Learning Systems and Data Mining courses
 |Supervisor=Sławomir Nowaczyk, Sepideh Pashami,
 |Level=Master
-|Status=Ongoing
+|Status=Finished
 }}
 This project aims at analysing historical records of workshop visits, detailing repair and maintenance work, for 70.000 Volvo trucks over a period of more than 10 years. The idea is to use data mining and machine learning techniques in order to find structure and regularities in this data. From both scientific and practical point of view, it is interesting to provide classification (either as a taxonomy or an ontology) of different service events.

@@ Line 2: / Line 2: @@
 |Summary=Finding good representations for data-driven description of Volvo truck's repair and maintenance history
 |Keywords=Data Mining, Knowledge Representation
-|TimeFrame=Spring 2015
+|TimeFrame=Spring 2016
 |References=http://www.sciencedirect.com/science/article/pii/S000437029800023X

@@ Line 13: / Line 13: @@
 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.335.8737
 |Prerequisites=Programming competence
-Cooperating Intelligent Systems and Learning Systems courses
+Artificial Intelligence, Learning Systems and Data Mining courses
 |Supervisor=Sławomir Nowaczyk, Rune Prytz
 |Level=Master