Fault classification of a centrifugal pump in normal and noisy environment with artificial neural network and support vector machine enhanced by a genetic algorithm

Abstract

Fault diagnosis and detection play a crucial role in every system for its safe operation and long life. Condition monitoring is an applicable and effective method of maintenance techniques in the fault diagnosis of rotating machinery. In this paper two outstanding heuristic classification approaches, namely Artificial Neural Network (ANN) and Support Vector Machine (SVM) with four different kernel functions are applied to classify the condition of a real centrifugal pump belonging to petroleum industry into five different faults through six features which are: flow, temperature, suction pressure, discharge pressure, velocity and vibration. To increase the power of our classifiers, they are trained and tuned by Genetic Algorithm (GA) which is an effective evolutionary optimisation method. The experiments are done once with normal data and another time with noisy data in order to examine how robust the approaches are. Finally, the classification results of ANN-GA, SVM-GA, pure ANN and SVM (without GA enhancements) along with other two practical classification algorithms, namely K-Nearest Neighbours (KNN) and Decisions Tree, are compared together in terms of different aspects