Pixel

Engineering

Biology

Identification (biology)

Regularization (linguistics)

Impeller

Grayscale

Computer science

Botany

Acoustics

Mechanical engineering

Physics

Kullback–Leibler divergence

Pattern recognition (psychology)

Artificial intelligence

Centrifugal pump

Artificial neural network

Convolutional neural network

Speech recognition

Convolution (computer science)

Applied Acoustics

An improved CNN is proposed for the diagnosis of defects in components of a centrifugal pump. The improvement is attained by modifying the cost function of CNN. For defect identification, first, grey scale acoustic images are obtained by processing acoustic signals using analytical wavelet transform (AWT). Second, a new entropy based divergence function, a type of regularization function is introduced in the cost function of CNN which avoids redundant activation of hidden layer in CNN, thus ensuring sparsity by reducing number of training parameters and avoid over-fitting problem in CNN. Third, modeling of improved CNN is done using grey scale acoustic images. After, training, finally test data is applied to improved CNN for the identification of defects. Experimental results achieved while diagnosis defects of centrifugal pump show that the proposed improved CNN has a significant improvement of identification accuracy of about 3.2% over traditional CNN.

Improved deep convolution neural network (CNN) for the identification of defects in the centrifugal pump using acoustic images