Date of Award
Winter 2023
Rights
Access is available to all users
Document Type
Thesis
Degree Name
Master of Science (MS) in Applied Mathematics
Department
Mathematics
Abstract
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.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Ofori-Addo, Eunice, "Modeling repairable system failure data using NHPP realiability growth mode." (2023). EWU Masters Thesis Collection. 880.
https://dc.ewu.edu/theses/880
Included in
Discrete Mathematics and Combinatorics Commons, Numerical Analysis and Scientific Computing Commons, Systems Architecture Commons
