Hydraulic pump failure results in a high rate of energy loss, performance degradation, high vibration levels, and continuous noise emission. An unexpected pump failure might result in a sudden collapse of the hydraulics, resulting in significant financial losses and the shutdown of the whole factory. Fault diagnosis plays a critical function in diagnosing flaws before they occur. Early detection is crucial for identifying problems and may save money, time, and potentially dangerous circumstances.

In recent years, many studies in intelligent fault diagnosis utilizing various machine learning approaches have been conducted. A vibration-based fault diagnosis in industrial mono-block centrifugal pumps is presented in this study. An experimental configuration for structuring databases, required for developing algorithms for running machine learning programs, is designed. Standard condition vibration signals are collected from the setup when the pump is healthy and free of defects. This study considers the two major defective conditions of broken impeller (B.I.) and seal failure (S.F.). The faults are introduced in the pump one after the other, and the vibration signals are obtained. The image processing approach converts these analog signals to 2D images.

Later, the images are trained and tested using a deep convolution neural network (DCNN) classifier, and the fault accuracy is verified. The results show an accuracy of 99.07% after training and testing the image dataset.

The suggested DCNN architecture exhibits high and accurate fault diagnosis accuracy for the industrial mono-block centrifugal pump.