Quadratic Support Vector Machine and K-Nearest Neighbor Based Robust Sensor Fault Detection and Isolation

Ahmed Abed,Abbas Issa,Sabah Gitaffa

doi:10.30684/etj.v39i5a.2002

Abstract

Fault detection plays a serious role in high-cost and safety-critical processes. There are two main drivers for continuous improvement in the area of early detection of process faults safety and reliability of technical plants. Detect fault in Geophone string sensors (SG-10) are very important in oil exploration to avoid loss economy. Methods are developed to enable earlier detection of process faults than the traditional limit and trend checking based on a single process variable and the development of these methods is a key matter. Classification methods will be used for pattern recognition and as such is appropriate for fault detection. In supervised training input-output pairs, both for normal and fault conditions, are presented to the network. The models were trained on the free fault and fault sensors. Then the Quadratic Support Vector Machine (QSVM) and k-Nearest Neighbor (KNN) as the classifiers are used. The test results for measuring the performance of 1232 sample classifiers from data show that the accuracy of fault-free sensor recognition is 97.4 % and 100% consecutively for these classifiers.

Highlights

The technical systems are increasingly sophisticated as the industry progresses
Little research on pattern recognition is available based on sensor fault detection and isolation methods
Trained Pattern Recognition (QSVM, K-Nearest Neighbor (KNN)) models will be used in the online Fault Detection and Isolation (FDI) process [17, 18]

Summary

INTRODUCTION

The technical systems are increasingly sophisticated as the industry progresses. The more complicated the system, the more vulnerable it is to faults. Little research on pattern recognition is available based on sensor fault detection and isolation methods. The distance between the samples of research and all the samples of training should be measured in order to find the k-nearest neighbors This requires a huge amount of computing overhead in the case of big data. As a nonparametric classification method, the KNN algorithm does not need a training process It does not require prior knowledge about the statistical properties of the training instances, and can directly classify the query based on the information provided by the training set [10]. This research will define the classification process of sensor fault detection and isolation with QSVM and KNN methods.

RELATED WORKS

THEORETICAL FOUNDATION

Classification Method

SIMULATION RESULTS

CONCLUSION