Fuzzy Recurrence Plots for Shallow Learning-Based Blockage Detection in a Centrifugal Pump Using Pre-Trained Image Recognition Models

Rags, dusts, foreign particles, etc., are the primary cause of blockage in the centrifugal pump and deteriorate the performance. This study elaborates an experimental and data-driven methodology to identify suction, discharge, and simultaneous occurrence of both blockages. The discharge pressure signals are acquired and denoised using CEEMD. The fuzzy recurrence plots obtained from denoised signals are attempted to classify using three pre-trained models: Xception, GoogleNet, and Inception. None of these models are trained on such images; thus, features are extracted from different pooling layers which include shallow features too. The features extracted from different layers are fed to four shallow learning classifiers: Quadratic SVM, Weighted k-nearest network, Narrow Neural network, and subspace discriminant classifier. The study finds that subspace discriminant achieves the highest accuracy of 97.8% when trained using features from second pooling of Xception model. Furthermore, this proposed methodology is implemented at other blockage conditions of the pump. The subspace discriminant analysis outperforms the other selected shallow classifier with an accuracy of 93% for the features extracted from the first pooling layer of the Xception model. Therefore, this study demonstrates an efficient method to identify pump blockage using pre-trained and shallow classifiers.