Scalability Analysis of Predictive Maintenance Using Machine Learning in Oil Refineries

Modern refineries typically use a high number of sensors that generate an enormous amount of data about the condition of the plants. This generated data can be used to perform predictive maintenance, an approach to predict impending failures and mitigate downtime in refineries. This research analyzes the scalability of machine learning methods for predictive maintenance solution in an oil refinery. It can be done by modeling the normal behavior of the plant and use the prediction error to identify anomalies which might potentially become failures. Several methods and learning algorithms are explored in this research to model the normal behavior of multiple components in the plant. The experiments are performed by using historical process data from a crude distiller unit at Shell Pernis Refinery. The results show that the proposed approach using multiple targets model is able to predict multiple components in the plant. It is not only able to detect anomalies but also identify the faulty component. Furthermore, it reduces the required time to model the normal behavior of the plant which improves the scalability of the predictive maintenance approach in the refinery.Computer Scienc

DUT-MMSR at MediaEval 2017: Predicting Media Interestingness Task

This paper describes our approach for the submission to the Media-eval 2017 Predicting Media Interestingness Task, which was particularlydeveloped for the Image subtask. An approach using a late fusion strategy is employed, combining classifiers from different features by stacking them using logistic regression (LR). As the task ground truth was based on pairwise evaluation of shots or keyframe images within the same movie, next to using precomputed features as-is, we also include a more contextual feature, considering aver-aged feature values over each movie. Furthermore, we also consider evaluation outcomes for the heuristic algorithm that yielded the highest MAPscore on the 2016 Image subtask. Considering results obtained for the development and test sets, our late fusion method shows consistent performance on the Image subtask, but not on the Video subtask. Furthermore, clear differences can be observed between MAP@10 and MAP scores.Multimedia Computin

DUT-MMSR at MediaEval 2017: Predicting Media Interestingness Task

This paper describes our approach for the submission to the Media-eval 2017 Predicting Media Interestingness Task, which was particularlydeveloped for the Image subtask. An approach using a late fusion strategy is employed, combining classifiers from different features by stacking them using logistic regression (LR). As the task ground truth was based on pairwise evaluation of shots or keyframe images within the same movie, next to using precomputed features as-is, we also include a more contextual feature, considering aver-aged feature values over each movie. Furthermore, we also consider evaluation outcomes for the heuristic algorithm that yielded the highest MAPscore on the 2016 Image subtask. Considering results obtained for the development and test sets, our late fusion method shows consistent performance on the Image subtask, but not on the Video subtask. Furthermore, clear differences can be observed between MAP@10 and MAP scores.</p