Assessment of Reliability and Remaining Fatigue Life of Topside Piping Using Dynamic Bayesian Network Hydrocarbon release due to Vibration Induced Fatigue (VIF) costs millions of dollars each year in inspection, maintenance and replacement activities, as well as lost production for oil and gas operators. Therefore, it is vital to assess the reliability of topside piping against VIF. In this context, the authors have utilized the fundamentals of probability, reliability and statistical methods, using Dynamic Bayesian Networks (DBNs) to estimate the reliability of process piping. Firstly, different sources of uncertainty, such as physical variability, statistical uncertainty, etc., in the crack growth process (Paris law is used to model the crack growth) are identified and quantified, with suitable distributions and parameters obtained from literature. Thereafter, a DBN is developed to obtain the distribution of the Remaining Fatigue Life (RFL). The results (in terms of crack size) are validated against experimental data. Thereafter, statistical methods are used to obtain the reliabilityPoF curve from the RFL distribution derived previously, which can be used to set up an inspection schedule, as illustrated in the case study. The advantage of using DBNs for reliability analyses lies in the ease of updating the prior information to obtain the posterior distributions.

Reliability Assessment of Mining System Based on Time Mining Data The degradation of mechanical systems is a typical phenomenon accompanying most systems. When considering dependability, safety and costeffectiveness, the degradation may result in serious consequences. Direct advancing degradation in all technical systems is not easy to observe. In order to do so, related information is used, e.g. the study of diagnostic and operation measuressignals. This article presents a study of the deterioration of a mining head with multitool knives. There is a dataset containing the records of the drilling head behaviour in standard operation. The records contain typical operation characteristics such as moment and power for both sharp and blunt knives. Degradation modelling of the studied mining head knives is performed with stochastic continuous diffusion processes. They are Pearson type, GaussMarkov type and Levy type processes. The achieved results are expected to be used for the observation of i) the first passage time of degradation critical value), ii) the prediction of residual useful life, and iii) the rationalization of in field operation and maintenance.