Can pretrained networks be used in fault diagnosis of monoblock centrifugal pump?

Abstract

Monoblock centrifugal pumps are used daily and play an essential role in agriculture, wastewater treatment, food, paper, gas and oil industries. Extended and endless use of monoblock centrifugal pump can affect the desired pump characteristics, compromising the efficiency of the pump. The condition of a pump is judged based on the high vibration, unusual noise, leakage, etc. Maintaining pump health is essential to increase the productivity in industry. Countless experiments were carried out by researchers to maintain the efficiency of the pump. The main shortcomings of traditional fault diagnosis are the need for a high degree of human intellect and professional knowledge. Such drawbacks have made the researchers provide autonomous and intelligent diagnostic tools. The objective of this article is to investigate the possibility of use of deep learning technique to identify the type of fault present in the monoblock centrifugal pump. First, vibration signals from the monoblock centrifugal pump were acquired and plotted as images that serve as an input for deep learning (DL) algorithms. The deep learning system can analyse the status of the monoblock centrifugal pump and learn from the vibration signal plots. In this paper, pre-trained networks such as ResNet-50, AlexNet, GoogleNet and VGG-16 were used to diagnose the fault in the monoblock centrifugal pump. The outcome of the hyperparameters namely learning rate (LR), batch size (BS), split ratio (SR) and solver were considered and the best performing network was recommended for the monoblock centrifugal pump fault diagnostics. Findings: A higher classification accuracy of 100% with lower training time of 177 s was obtained while using ResNet-50.