Why Stochastic Models That Are So Famous, Become Infamous In Reliability Engineering

Stochastic models have interesting applications in predicting random behavior for varied problems of engineering and sciences. These models are defined as a family or collection of a set of random variables defined on a time dependent sample space, a sample space also known as state space. In reliability engineering, evaluating the performance of a system for a specified time using stochastic models started in the twentieth century. Further different forms of stochastic models like Markov chain, renewable models, regenerative models were used in performance evaluation for system improvements. Studies on these models have shown tremendous capabilities of evaluating performance of a simple system to complex systems. However, it is failing to attract the majority of current practitioners as well as academic researchers for bringing more application oriented or improved work based on these models from the last few decades/years (ref. number of publications in top reliability journal viz RSS, IEEER, Microelectronics Reliability). This paper seeks to understand why these models are so famous in reliability engineering in the early years of the reliability discipline and, becoming infamous today as per collected statistics of academic literature, as well as diverting the mindset of the scientific academic community towards other approaches. It also provides a comparative discussion on the model research that has been carried out so far and discusses future insights on how it can serve as a better model to estimate reliability using a hybrid technique for big industrial systems process.