Supporting complete vehicle reliability forecasts

• Main Author: Lindén, Julia
• Format: Others
• Language: English
• Published: KTH, Maskinkonstruktion (Avd.) 2017
• Subjects: Reliability forecast; interactive function; operating conditions; Mechanical Engineering; Maskinteknik
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208195; http://nbn-resolving.de/urn:isbn:978-91-7729-400-9

Reliability is one of the properties that customers of heavy trucks value highest.Dependent on all parts and functions of the vehicle, reliability is a complexproperty, which can normally be measured only towards the end of a developmentproject. At earlier development stages, forecasts can give valuable decision supportfor project planning.The main function of a heavy truck is to transport goods, but the truck also hasinteractive functions as the working environment of the driver. Interactivefunctions are functions experienced by the driver. They are subjective, in the senseof being person dependent, so that a system can be experienced as inadequate byone user but satisfactory by another. Examples of interactive functions of heavytrucks are climate comfort and ergonomics, which are experienced differently bydifferent drivers. Failures of these functions lead to costs and limited availabilityfor the customer. Therefore it is important to include them in reliability forecasts.The work described in this thesis concerns some elements of the system reliabilityforecast. Two studies are presented, one proposing a qualitative systemarchitecture model and the other reviewing and testing methods for evaluating theimpact of varying operating conditions. Two case studies of a truck cab in a systemreliability test were made. The first case study shows that the system architecturemodel supports reliability forecasts by including interactive functions as well asexternal factors, human and environmental, which affect function performance.The second case study shows that modelling uncertainty is crucial for interactivefunctions and recommends a method to forecast function performance while takingvarying operating conditions into account. === <p>QC 20170602</p>