Precision fault prediction in motor bearings with feature selection and deep learning

Abstract

In the disciplines of industrial machinery, mechanical engineering is beneficial to recognize motor performance for motors with HP power, torque transducer, dynamometer, and control electronics. The motivation is to address the need for more accurate and efficient fault prediction in machinery to prevent breakdowns, reduce maintenance costs, and improve overall reliability. In this work, deep learning classifiers used to classify ball defect inner race fault, outer race fault and normal motor performance in testing. With the aid of three distinct classifiers CNN, FFNN, and RBN; these suggested relative characteristics are assessed. In comparison to other current algorithms, the suggested methodology for classifying motor performance achieved maximum accuracy in each CNN test at 95.4% and 97.7%. The correlation and chi-square algorithms are used to find out the added characteristics and rank of features. The correlation technique provides relations between attributes, and the chi-square offers the optimal balance between precision and feature space. We discovered that the performance is enhanced overall by relative power characteristics. The suggested models might offer rapid responses with less complexity.