Hongyang Yu

A flexible hierarchical Bayesian modeling technique for risk analysis of major accidents

Safety analysis of rare events with potentially catastrophic consequences is challenged by data scarcity and uncertainty. Traditional causation-based approaches, such as fault tree and event tree (used to model rare event), suffer from a number of weaknesses. These include the static structure of the event causation, lack of event occurrence data, and need for reliable prior information. In this study, a new hierarchical Bayesian modeling based technique is proposed to overcome these drawbacks. The proposed technique can be used as a flexible technique for risk analysis of major accidents. It enables both forward and backward analysis in quantitative reasoning and the treatment of interdependence among the model parameters. Source-to-source variability in data sources is also taken into account through a robust probabilistic safety analysis. The applicability of the proposed technique has been demonstrated through a case study in marine and offshore industry.

A Novel Semiparametric Hidden Markov Model for Process Failure Mode Identification

The emitting distributions of a hidden Markov model (HMM) are normally constructed using the cross moments of the process variables. Similar to the mean of a univariate probability distribution, the cross moment is the most fundamental statistic of a multivariate probability distribution, which is not capable of capturing the high-order statistical features of process data. To alleviate this limitation, the high-order equivalence of the cross moment demonstrated in this paper, as the complete dependence structure, is used to construct the emitting distribution for HMM. The complete dependence structure among the process variables is modeled in a Gaussian copula. A semiparametric data transformation is also proposed to ensure the necessary conditions for using a Gaussian copula are met. The final emitting distribution is constructed as a finite mixture of the copula models. The proposed HMM is tested on two industrial studies for performance validation.Note to Practitioners—Gaussian mixture model HMM (GMM-HMM) is an efficient and easy-to-implement tool for mode identification of dynamic industrial processes. These virtues of GMM-HMM mainly come from the use of Gaussian emitting distribution, which allows closed-form derivatives to be computed for the expectation and maximization (EM) estimation of mode parameters. The main novelty of the proposed copula mixture model HMM (SPCMM-HMM) is twofold; its emitting distribution also belongs to the exponential family, thus enabling efficient estimation of parameters through an exact EM procedure; meanwhile, it is also capable of characterizing complex dependence structures among process variables. However, the proposed SPCMM-HMM has more model parameters compared with the GMM-HMM. It excels in situations where the operation of the process system being monitored is highly integrated (with complex process variable interactions) and abundance of training data samples is available. In this paper, the interactions between process variables in both case studies are complex due to the implementation of multiple closed control loops, and a large amount of training data samples can be easily generated from simulation. The performance of the SPCMM-HMM is shown to be consistently better than that of the GMM-HMM under such settings, which are fairly common in modern industrial processes. Nonetheless, if the process system being considered is only designed for a simple operation or there is a scarcity of training data samples, the GMM-HMM is still the preferred method as it is less prone to overfitting.

Chapter 5 - Reactive approaches of probability update based on Bayesian methods

Dynamic safety analysis in chemical and process facilities is necessary to prevent unwanted events that may cause catastrophic accidents. Probability updating and adapting of stochastic events and dynamic processes, which evolve over time, are the key to dynamic safety analysis. Conventional risk assessment techniques, such as fault tree, event tree, and bow-tie analyses, have long been used for effective safety analysis of process plants; however, owing to their static characteristics, their application to dynamic safety analysis has been relatively limited. Bayesian methods, such as hierarchical Bayesian analysis and Bayesian network, are effective techniques with ample potential for application in dynamic safety analysis. This chapter is aimed at presenting the state-of-the-art application of Bayesian analysis and especially the Bayesian network method in dynamic safety analysis of process systems.

An integrated approach for system functional reliability assessment using Dynamic Bayesian Network and Hidden Markov Model

This paper presents a novel methodology to estimate and predict the functional reliability of a system using system functional indicators and condition indicators of components. Instead of ‘system reliability’, the paper uses the terminology ‘system functional reliability’ because the functional indicators used in the methodology principally represent the system performance level or system functionality. The proposed model relates the degradation state of components to the system functional state. The model allows the use of system functional indicators and condition data of components in continuous time domain. The proposed methodology uses both Hidden Markov Model and Dynamic Bayesian Network for estimating and predicting system functional reliability. HMM helps in mapping the continuous data into hidden state probabilities while the system DBN helps in finding the posterior system state probability by considering the component dependencies within a system. The study is also extended to show how the external covariates can be incorporated into the proposed model. Since the external covariates accelerate the degradation of a component, the component state transition probability in the second model is adjusted to vary with respect to the covariates. A case study based on Tennessee Eastman Chemical Process is conducted to demonstrate the proposed methodology for system functional reliability estimation and prediction. Another simulation based case study is presented to describe how the external covariates are included in the presented methodology.