Bayesian Weapon System Reliability Modeling with Cox-Weibull Neural Network

• URL: http://arxiv.org/abs/2301.01850v5
• Date: Fri, 14 Apr 2023 17:11:53 GMT
• Title: Bayesian Weapon System Reliability Modeling with Cox-Weibull Neural Network
• Abstract summary: We propose to integrate weapon system features into a parameterized Cox-Weibull reliability model via a neural network. In parallel, we develop an alternative Bayesian model by parameterizing the Weibull parameters with a neural network.
• Score: 1.0152838128195467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose to integrate weapon system features (such as weapon system manufacturer, deployment time and location, storage time and location, etc.) into a parameterized Cox-Weibull [1] reliability model via a neural network, like DeepSurv [2], to improve predictive maintenance. In parallel, we develop an alternative Bayesian model by parameterizing the Weibull parameters with a neural network and employing dropout methods such as Monte-Carlo (MC)-dropout for comparative purposes. Due to data collection procedures in weapon system testing we employ a novel interval-censored log-likelihood which incorporates Monte-Carlo Markov Chain (MCMC) [3] sampling of the Weibull parameters during gradient descent optimization. We compare classification metrics such as receiver operator curve (ROC) area under the curve (AUC), precision-recall (PR) AUC, and F scores to show our model generally outperforms traditional powerful models such as XGBoost and the current standard conditional Weibull probability density estimation model.

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