Artificial neural network based classification of faults in centrifugal water pump

The detection and diagnosis of faults are of great practical significance for the safe operation of a plant. Early detection of fault can help avoid system shutdown, breakdown and even catastrophe involving human fatalities and material damage. This paper presents the design and development of ANN-based model for the fault detection of centrifugal water pump using a back-propagation learning algorithm and multi-layer perceptron neural network. The centrifugal pump conditions were considered to be healthy pump and faulty impeller and faulty seal and cavitation, which were four neurons of output layer with the aim of fault detection and identification. Features vector, which is one of the most significant parameters to design an appropriate neural network, was extracted from analysis of vibration signals in frequency domain by means of FFT method. The statistical features of vibration signals such as mean, standard deviation, variance, skewness and kurtosis were used as input to ANN. Different neural network structures are analyzed to determine the optimal neural network with regards to the number of hidden layers. The results indicate that the designed system is capable of classifying records with 100 % accuracy with one hidden layer of neurons in the neural network.