Quadratic support vector machine and k-nearest neighbor based robust sensor fault detection and isolation

Abstract EN

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