A data quality management framework for equipment failure risk estimation: Application to the oil and gas industry

In engineering asset management, accurate failure risk estimation is essential for averting equipment breakdowns and optimizing risk-based maintenance strategies. Data quality and model fitting are two critical sources of prediction uncertainty in risk estimation. While much attention has been devoted to model fitting for risk estimation, the critical role of data quality has often been overshadowed. To bridge this research gap, this paper presents a novel data quality management framework tailored for industrial equipment failure risk estimation. The framework covers the steps from data to model. It consists of the following main phases: data development, data quality assessment, data quality requirement decision-making, data quality improvement, and risk estimation model development. The framework provides detailed guidelines that can facilitate data practitioners to build individualized data quality requirement decision-making models for failure risk estimation of their equipment. The decision-making model can measure the adequacy of existing data to build a risk estimation model that meets the specified requirements and further determines the best risk estimation model given the available data. A case study using actual data collected during oil well drilling operations from multiple oil fields demonstrates the practicality and effectiveness of the framework. In this case study, four risk estimation models are compared, including two baseline models (mean time to failure and median time to failure) and two machine-learning models (quantile regression and hidden Markov model). In addition, a decision tree-based decision model is developed to determine whether the data quality meets the requirements and the best risk estimation model in case the data quality meets the requirements. •Describe data quality metrics for industrial equipment failure risk estimation.•Introduce a novel equipment failure risk estimation model performance indictor.•Propose a data quality management framework for failure risk estimation.•The framework is validated on electronic board field failure data.