Digitalization of Subsea Maintenance - Application of Industry 4.0 in Prognostics of Technical Condition and Residual Life

Abstract

The objective for the thesis is to contribute to enhanced production safety and availability of Equinor's subsea equipment. Through introducing methods, providing new technologies and concepts, the study highlights challenges and recommends improving measures regarding subsea assets in a lifelong impact.

Today the Subsea Control Module (SCM) is subject to corrective maintenance in Equinor's operation. The SCM is considered a production critical equipment, and experience arbitrary failures. This entails shut-down of production, and subsequent financial losses. Operational data for the SCMs is trended and analyzed to identify correlation to historical failure. The analysis revealed no correlation.

The study presents models applicable for predicting failure and estimating residual life. The current situation in Equinor is not sufficient in predicting failure of electrical components. This thesis identifies shortcomings of Equinor's operation, and provide guidance to enhance predictive abilities and maintenance performance. The thesis suggests a future-oriented maintenance concept to meet and overcome these obstacles. Predictive Analytics will form the basis in this concept, complemented by technologies within Maintenance 4.0. The following recommendations is given:

- Conduct Predictive Analytics pilot study on SCM using Machine Learning algorithms and pattern recognition. - Enhance knowledge of electrical component degradation \& visualization of trended parameters to provide the ability to estimate Remaining Useful Life (RUL). - Implement Predictive Maintenance aspects into the maintenance management loop. Predictive Analytics and Machine Learning ensure required technical condition. - Overcome shortcomings and implement proposed concept. It is recommended to hybrid machines as a part of the decision making, together with operators. Digital Twin will serve as a tool for visualization and assistance.