Reliability testing and validation are essential parts of any automobile
development project. These tests mainly depend on two things: how much of the
product’s target life is covered and whether enough samples are tested to
capture variations. Today, most projects face tight timelines and cost
constraints, which makes it important to design an optimized reliability test
plan.
Accelerated testing is widely used to reduce testing time, but finding the
correct acceleration factor for a complex system like an automotive engine is
difficult. Existing studies describe methods such as simple stress tests,
step-stress tests, and degradation tests to shorten test duration. However,
these methods need more samples at different stress levels to establish a clear
relationship between stress and life and then calculate the acceleration factor.
While such models work well at the component level, they become much more
complicated at the system level because different components can have different
acceleration behaviors.
In this paper, a method is proposed to derive the acceleration factor by
correlating engine test bed data with field failure and usage data. Test
engineers often use standard accelerated test cycles for engines used in similar
applications. As a result, organizations usually have historical test data and
field data from earlier, similar products. The proposed approach models
accelerated test data and field failure data separately and then links them to
determine the acceleration factor. This derived factor can later be used in
future engine development projects to convert test cycles into equivalent field
mileage coverage. Weibull analysis and regression techniques are used to support
the case studies presented in this work.