Unsupervised Real Time Prediction of Faults Using the Support Vector Machine

TL;DR

This paper enhances fault prediction in oil and gas equipment by optimizing SVM classification with SMO training and ensemble methods, significantly improving accuracy on imbalanced datasets.

Contribution

It introduces the use of SMO training for SVM in fault prediction and combines it with ensemble techniques to handle imbalanced data effectively.

Findings

SVM with SMO outperforms standard SVM in fault classification.

Ensemble stacking improves predictive accuracy on imbalanced datasets.

The proposed model surpasses conventional classifiers in fault detection.

Abstract

This paper aims at improving the classification accuracy of a Support Vector Machine (SVM) classifier with Sequential Minimal Optimization (SMO) training algorithm in order to properly classify failure and normal instances from oil and gas equipment data. Recent applications of failure analysis have made use of the SVM technique without implementing SMO training algorithm, while in our study we show that the proposed solution can perform much better when using the SMO training algorithm. Furthermore, we implement the ensemble approach, which is a hybrid rule based and neural network classifier to improve the performance of the SVM classifier (with SMO training algorithm). The optimization study is as a result of the underperformance of the classifier when dealing with imbalanced dataset. The selected best performing classifiers are combined together with SVM classifier (with SMO training algorithm) by using the stacking ensemble method which is to create an efficient ensemble predictive model that can handle the issue of imbalanced data. The classification performance of this predictive model is considerably better than the SVM with and without SMO training algorithm and many other conventional classifiers.