Modelling condition monitoring inspection using the delay-time concept

Wang, W

Abstract

In the literature on inspection modelling, the failure distribution
traditionally plays a fundamental role in model construction in that it
is assumed that system failures occur instantly at random time points
from new with a known pdf. of time to failure. Numerous models have
been built on this basis. However, Professor Christer challenged this
traditional idea and proposed the concept of delay time. The idea,
which is an essential part of most engineers' experience, assumes that
defects do not just appear as failures, but are present for a while
before becoming sufficiently obvious to be noticed and declared as
failures. The time lapse from when a defect could first be identified
at an inspection to consequential failure has been termed the "delay
time". It is this idea which can be captured to reveal the nature and
scope for preventive maintenance or inspection. It appears that the
concept is now being taken up by many other authors.
In this thesis, various models for condition monitoring inspection are
built on the basis of delay time analysis. Extensions and further
developments are made here to enrich the delay-time modelling. Since
the distribution of the delay time is important to delay time
modelling, a new approach to estimate the delay time distribution is
proposed. This technique, which contrasts with the previous subjective
data estimation technique, is based upon objective data.
Assuming the distribution of the delay time is known, models of
condition monitoring inspection are fully discussed for both perfect
and imperfect inspections, and for infinite and finite time horizons.
Based upon the models for perfect inspection, algorithms are presented
to find the optimal solution. Numerical examples are presented in each
Chapter to illustrate how models and algorithms work.

Citation

Wang, W. (1992). Modelling condition monitoring inspection using the delay-time concept. (Thesis). University of Salford, UK

• Thesis Type: Thesis
• Deposit Date: Jun 29, 2009
• Publicly Available Date: Jun 29, 2009
• Additional Information: Additional Information : PhD supervisor: Professor A. H. Christer