## 2 Literature Review
Subsequently, a mathematical reliability formula was derived from the exponential distribution to optimize the need for preventive maintenance. The above study, unlike this research, focuses on water pipelines rather than oil and gas pipelines. However, its analytical approach of the preventive maintenance can be used in this research to recommend a planning framework for the preventive maintenance of the unloading system. [PERSON] et al. [18] developed a comprehensive method for assessing reliability in energy pipeline systems scientifically, which can be applied to oil and gas systems. Basically, the failure in different mechanisms and the pipes structural integrity are considered as the base to assess system reliability. The study is centered around three types of analyses: mathematical, thermal-hydraulic, and pipeline structure analyses. The authors used homogeneous and non-homogeneous Poisson distributions with a constant failure rate assumption to give a better insight into the likelihood of future failures in the pipeline network system. Interestingly, they are confident that the developed method can be applied under any environmental condition. It was found that pipe corrosion constitutes around 30-40% of the total pipeline failures. Moreover, the frequency of failures in pipelines increases when they exceed 30 years in service. The assumption of a constant failure rate throughout the pipeline is the main assumption of this research. The approaches taken in this study assumed a constant failure rate and consideration of different mechanisms, like what we have done in the unloading application, that has enlightened the context of this research. [PERSON] et al. [19] discussed the reliability distributions, such as exponential, normal, Rayleigh, logarithmic, and Weibull. The authors highlighted that despite the fact that the usage of exponential distribution under the assumption of constant failure rate of equipment is beneficial during the normal operation life, it is important to apply other distributions to further study the failure behavior of wearing and change of properties of the equipment by applying normal and Weibull distributions. Moreover, this gives a better understanding of the failure prediction under varying working conditions and changing loads on the equipment. An algorithm has been developed that analyzes the technological parameters, which helps in the selection of the appropriate distribution function. The main purpose of this study is to provide operating entities in the pipeline transportation industry with mathematical model to better predict the likelihood of failure of their equipment under dynamic operating conditions. [PERSON] et al. [20] proposed a reliability-based maintenance approach for the deterioration of civil infrastructure systems addressing different uncertainties. These uncertainties include resistances and time loads changes, and interventions of the maintenance series that are used to save the systems. The authors used computer software for this analysis. [PERSON] et al. [21] introduced a new model of cost effective maintenance strategies to identify an acceptable period of the monitoring conditions and level of deterioration after incomplete preventive repairs. The models on maintenance optimization for the production system were developed by [PERSON] and [PERSON] respectively using the various methods [22; 23]. [PERSON] et al. [24] proposed an opportunity maintenance model for the production line to carry out preventive maintenance on several machines simultaneously, when one machine in the production line fails. Nonetheless, the maintenance technique is difficult to use for a complicated line consisting of a large number of machines. [PERSON] et al. [25] developed an opportunistic maintenance strategy for wind turbines addressing an inconsistent maintenance plan dependent on reliability. The authors characterized this maintenance and illustrated the consequences of corrective maintenance action using a hybrid model based on the hazard rate.