A Bayesian inference-based approach for performance prognostics towards uncertainty quantification and its applications on the marine diesel engine

Abstract

In this paper, the Bayesian analysis is introduced for the performance prognostics of the marine diesel engine to address the uncertainty of inferences and results by using probability distributions. Two Bayesian models are presented: the Bayesian neural networks model is used to implement health monitoring whilst the Bayesian logistic regression model quantifies the run-to-failure process of the marine diesel engine. The Variational Inference and the Markov Chain Monte Carlo algorithms learn and infer these two models' parameters, respectively. Additionally, by analyzing characteristics of the marine diesel engine, the instantaneous angular speed signals are selected as the condition monitoring data, which can be used to indirectly predict the indicated mean effective pressure and further assess the performance of the marine engine. To verify the superiority of the proposed framework based on the Bayesian models and indirect estimation, operational datasets from a real engine under normal and fault conditions are acquired. The proposed framework and other conventional methods are adopted to analyze the attained data. The results demonstrate that the proposed approach is superior to the other methods and has the potential to be applied as an on-line condition monitoring tool for the performance prognostics of the marine diesel engine.

Keywords: Bayesian inference; Logistic regression function; Neural networks; Performance prognostics; Uncertainty quantification.