Condition monitoring of water pump bearings using ensemble classifier

The bearings faults are reported to be the major reason for centrifugal pump (CPs) failures. Limited literature is available to diagnose the minor scratches in the bearing surface through non-intrusive condition monitoring techniques. Recent research on the analysis of bearing scratches through non-intrusive motor current analysis (MCA) has shown encouraging results where the comparison of machine learning and convolutional neural networks (CNNs) was performed in the classification of healthy bearings and faulty bearings (holes and scratches). The fault classification accuracy of 89.26% through MCA combination with machine learning and CNN algorithm was reported which is very low. The key factors of low accuracies were identified as low amplitudes of the harmonics in the MCA spectrum, the magnitude of environmental noise, and utilization of conventional feature extraction techniques. This problem has been tackled in this paper by developing a novel feature extractor (NFE) that extracts powerful features from the integrated current and voltage sensors data. The NFE has been derived using the threshold-based decision mechanism which has the capability to identify the location of the feature harmonic, feature extraction, measure the amplitude of the fault component, and compare it with the derived threshold. The experimental data has been collected for the bearing balls (BB), bearing cage (BC), inner race (IR) and the outer race (OR) faults, and the performance of the NFE has been tested on an ensemble classifier (CatBoost) and the better classification accuracy (99.2% for an individual feature and 100% with the combination of two or more features) of NFE has been achieved as compared to previously reported methods.

Automated summary

Bearing faults are a major cause of centrifugal pump failures. Limited literature exists on diagnosing minor scratches on bearing surfaces through non-intrusive monitoring techniques. Recent research has shown promising results in analyzing bearing scratches using machine learning and convolutional neural networks (CNNs). However, the reported fault classification accuracy is low due to factors such as low harmonic amplitudes, environmental noise, and conventional feature extraction techniques. This paper addresses these challenges by developing a novel feature extractor (NFE) that extracts powerful features from integrated current and voltage sensor data, achieving significantly improved classification accuracy compared to previous methods.