Data-driven predictive model of reliability estimation using degradation models: a review

Abstract

The concept of the industrial internet of things (IIoT) provides the foundation to apply data-driven methodologies. The data-driven predictive models of reliability estimation can become a major tool in increasing the life of assets, lowering capital cost, and reducing operating and maintenance costs. Classical models of reliability assessment mainly rely on lifetime data. Failure data may not be easily obtainable for highly reliable assets. Furthermore, the collected historical lifetime data may not be able to accurately describe the behavior of the asset in a unique application or environment. Therefore, it is not an optimal approach anymore to estimate the reliability based on classical models. Fortunately, most of the industrial assets have performance characteristics whose degradation or decay over the operating time can be related to their reliability estimates. The application of the degradation methods has been recently increasing due to their ability to keep track of the dynamic conditions of the system over time. This study reviews general approaches for the most important degradation-based reliability estimation models proposed by several researchers during last few decades. The most commonly applied deterministic and stochastic degradation models are reviewed in this study. Furthermore, a roadmap for adopting the degradation-based reliability estimation models based on the concept of the IIoT is explained in detail.