Big-Data Analytics for Predictive Maintenance Modeling: Challenges and Opportunities

Topics: Data and information management Facilities planning and maintenance
Getty Images

Big-data analytics can allow a better understanding of a production system’s abnormal behavior. This knowledge is essential for the adoption of a proactive maintenance approach, leading to a shift toward condition-based maintenance (CBM). CBM focuses on performing interventions on the basis of actual and future states of a system determined by monitoring underlying deterioration processes. One of the building blocks of CBM design and implementation is the prognostic approach, which aims to detect, classify, and predict critical failures. This paper presents approaches for constructing a prognostic system.


Optimization of maintenance costs is among operators’ main concerns in the search for operational efficiency, safety, and asset availability. The ability to predict critical failures emerges as a key factor, especially when reducing logistics costs is mandatory.

Experience has shown that significant benefits can be achieved when major maintenance interventions (overhauls, usually performed periodically) can be postponed on the basis of conclusions from the use of degradation models. Such an approach can be complex, but its results may reduce maintenance and logistics costs while keeping availability within required levels.

Traditional approaches to reliability estimations are based on the distribution of event records of a population of identical units. Many parametric failure modes, such as Poisson, exponential, Weibull, and log-normal distributions, have been used to model machine reliability. This project attempts to create an integrated solution using big data and analytics techniques to implement a CBM standard procedure for the target problem.

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper OTC 26275, “Big Data Analytics for Predictive Maintenance Modeling: Challenges and Opportunities,” by I.H.F. Santos, M.M. Machado, E.E. Russo, D.M. Manguinho, V.T. Almeida, R.C. Wo, M. Bahia, and D.J.S. Constantino, Petrobras; D. Salomone, M.L. Pesce, C. Souza, and A.C. Oliveira, EMC—Brazil Research Center; and A. Lima, J. Gois, L.G. Tavares, T. Prego, S. Netto, and E. Silva, PEE-COPPE/UFRJ, prepared for the 2015 Offshore Technology Conference Brasil, Rio de Janeiro, 27–29 October. The paper has not been peer reviewed. Copyright 2015 Offshore Technology Conference. Reproduced by permission.
This article is reserved for SPE members and JPT subscribers.
If you would like to continue reading,
please Sign In, JOIN SPE or Subscribe to JPT

Big-Data Analytics for Predictive Maintenance Modeling: Challenges and Opportunities

15 September 2016

Volume: 68 | Issue: 10