Fault detection in centrifugal pumping systems using neural networks

Fault detection and diagnosis of technical plants is of great importance for the safe operation and long life. An early detection of faults may help to avoid product deterioration, performance degradation, damage to the machinery and damage to human operators. This paper presents the design and development of Artificial Neural Network (ANN)-based model for the fault detection in centrifugal pumping system. The network is developed to detect a total of 20 faults. The training and testing data required to develop the neural network model were generated at different operating conditions by running the pumping system and by creating various faults in real time in a laboratory experimental model. A principal component analysis-based feature extraction method is proposed to reduce the dimension of the input features. The performance of the trained network is found to be satisfactory for the real-time fault diagnosis.