System availability assessment using a parametric Bayesian approach: a case study of balling drums

Abstract

Assessment of system availability usually uses either an analytical (e.g., Markov/semi-Markov) or a simulation approach (e.g., Monte Carlo simulation-based). However, the former cannot handle complicated state changes and the latter is computationally expensive. Traditional Bayesian approaches may solve these problems; however, because of their computational difficulties, they are not widely applied. The recent proliferation of Markov Chain Monte Carlo (MCMC) approaches have led to the use of the Bayesian inference in a wide variety of fields. This study proposes a new approach to system availability assessment: a parametric Bayesian approach using MCMC, an approach that takes advantages of the analytical and simulation methods. By using this approach, mean time to failure (MTTF) and mean time to repair (MTTR) are treated as distributions instead of being ''averaged'', which better reflects reality and compensates for the limitations of simulation data sample size. To demonstrate the approach, the paper considers a case study of a balling drum system in a mining company. In this system, MTTF and MTTR are determined in a Bayesian Weibull model and a Bayesian lognormal model respectively. The results show that the proposed approach can integrate the analytical and simulation methods to assess system availability and could be applied to other technical problems in asset management (e.g., other industries, other systems).

Maintenance Strategy Choice Supported by the Failure Rate Function: Application in a Serial Manufacturing Line

“…Table 2 presents parameters of distributions (Weibull, exponential or lognormal) and 95% confidence intervals, the expected values, and availabilities calculated after the models. Considering the exponential as a particular case of the Weibull distribution, the findings are consistent with Saari et al (2019), who reported a case in the mining industry in which the Weibull and lognormal are the most likely distributions to fit respectively TBF and TTR. In our case, when the Weibull model fits, the availability did not differ from the previous, simpler calculation.…”

“…Ribeiro et al (2019) reported a case in which the overall Av of a serial production line of an auto parts manufacturer ranged between 94% and 98%. Saari et al (2019) reported a study in which the Av of five individual balling drums in the mining industry ranged between [92% and 96%]. As the five machines form a parallel array, the systemic Av overcomes 99%.…”