Modelling uncertainty of reliability forecasts with varying operating conditions

• Heavy truck customers attach great importance to reliability, which make reliability assessments essential in product development projects. Since changes are easier and less expensive in early project stages, early reliability assessments are valuable. At these early stages, complete vehicle testing cannot yet be made. System reliability assessments must be made based on test data from component and subsystem tests, sometimes performed with different operating conditions than the system will be used in. Test data must be translated to the new situation, which requires information about how various factors affect reliability. Furthermore, the uncertainty in the forecast increases when the assessment is made for new operating conditions. Therefore, we also need information about how uncertainty propagates. The question is how this translation can be made, when data is sparse and expert judgement must be used, and how the increasing uncertainty can be reasonably modelled. In this paper, current methods to take into account varying operating conditions have been reviewed, and four methods have been tested in a case study. These methods are one based on fuzzy logic, a first-order second-moment reliability method (VMEA), and two variants of the proportional hazards model. The study shows that several methods are capable of handling sparse data, but only VMEA can model how uncertainty increases when operating conditions vary. It has however the drawback of being quite sensitive to uncertainty in the input data.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Maskinteknik -- Tillförlitlighets- och kvalitetsteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Mechanical Engineering -- Reliability and Maintenance (hsv//eng)

Nyckelord

Machine Design

Maskinkonstruktion

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