Abstract
The reliable and cost-effective condition monitoring of the bearings installed in water pumps is a real challenge in the industry. This paper presents a novel strong feature selection and extraction algorithm (SFSEA) to extract fault-related features from the instantaneous power spectrum (IPS). The three features extracted from the IPS using the SFSEA are fed to an extreme gradient boosting (XBG) classifier to reliably detect and classify the minor bearing faults. The experiments performed on a lab-scale test setup demonstrated classification accuracy up to 100%, which is better than the previously reported fault classification accuracies and indicates the effectiveness of the proposed method.
Highlights
Induction motors have a simple, cost-effective design and they are easy to manufacture
The stator, rotor and bearings are the main components of the induction motor
Severe defects can cause a breakdown of the motor, which can create huge losses for the industry in terms of maintenance time, production stops, material waste and delay in the scheduled delivery of products [3,4,5,6,7,8]
Summary
Induction motors have a simple, cost-effective design and they are easy to manufacture. The accuracy of the vibration analysis method is related to the accuracy of the accelerometer installation on the motor bearing Another issue with vibration-based diagnostics is the high cost of accelerometers. The non-intrusive motor current analysis technique is quite inexpensive; its reliability is affected by environment noise while detecting minor bearing faults. There are various types of algorithms for machine learning and deep learning and the selection of the algorithm is a challenging task Several factors, such as reliability, accuracy and processing time, should be considered to make the condition monitoring system compatible with the industry requirements. The aim of this study was to diagnose bearing faults of minor sizes using non-invasive instantaneous power analysis (IPA) as a signal processing tool, SFSEA as a feature extraction tool and XGB as a machine learning tool.
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