Modeling repairable system failure data using NHPP reliability growth mode.

<p>Stochastic point processes have been widely used to describe the behaviour of repairable systems. The Crow nonhomogeneous Poisson process (NHPP) often known as the Power Law model is regarded as one of the best models for repairable systems. The goodness-of-fit test rejects the intensity function of the power law model, and so the log-linear model was fitted and tested for goodness-of-fit. The Weibull Time to Failure recurrent neural network (WTTE-RNN) framework, a probabilistic deep learning model for failure data, is also explored. However, we find that the WTTE-RNN framework is only appropriate failure data with independent and identically distributed interarrival times of successive failures, and so cannot be applied to nonhomogeneous Poisson process.</p>

Name thesis:degree_name

Master of Science (MS) in Applied Mathematics

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Mathematics

Year

2023

Author dc:creator

• Ofori-Addo, Eunice

• Discrete Mathematics and Combinatorics
• Numerical Analysis and Scientific Computing
• Systems Architecture

Statement dc:rights

• Access is available to all users

Repository record dc:identifier

https://dc.ewu.edu/theses/880

OAI identifier oai:identifier

oai:dc.ewu.edu:theses-1880