CNN-based Single-Fault Diagnosis of Self-Priming Centrifugal Pump
Sandip Kumar Singh
1RSandip Kumar Singh, Department of Mechanical Engineering, V B S Purvanchal University Jaunpur (U.P.), India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 1839-1848 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1473109119/2019©BEIESP | DOI: 10.35940/ijeat.A1473.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: CNN is a very powerful deep learning technique for classification when the size of data is significant. It has been observed that it fails to give any reasonable classification when size of the data is small. This paper deals with an enhanced data technique, which is very useful for smaller size of available data. We proposed to increase the size of data to multiple times until a good classification accuracy is acquired. The paper shows that the neural networks perform very efficiently when such type of enhancement is done. It has been elaborated for evaluating the classification of faults of centrifugal pumps. The CNN-2D and CNN-1D yield 100% accuracy for diagnosing the faults of in this case. The performance is also compared with that of ANN. The number of epochs required to reach 100% accuracy for multiple different sizes of data is used to evaluate the performance. The enhanced data approach also shows that there is a drastic fall in overall classification time of CNN.
Keywords: Artificial Neural Networks (ANN), Convolution Neural Network (CNN), Multinomial Logistic Regression (MLR) Support Vector Classification (SVC).