Gear Fault Detection Using Machine Learning Techniques- A Simulation-Driven Approach

Machine learning (ml) based condition monitoring and fault detection of indust rial equipment is the current scenario for maintenance in the era of indust ry-4.0. the application of ml techniques for automatic fault detection minimizes the unexpected breakdown of the system. however, these techniques heavily rely on the historical data of equipment for its t raining which limits its widespread application in indust ry. as the historical data is not available for each industrial machine and generating the data experimentally for each fault condition is not viable. therefore, this challenge is addressed for gear applicat ion with tooth defect. in this paper, ml algorithms are trained using simulated vibration data of the gearbox and tested with the experimental data. simulated data is generated for the gearbox with different operating and fault conditions. a gearbox dynamic model is ut ilized to generate simulated vibrat ion data for normal and faulty gear condition. a pink noise is added to simulated data to improve the exactness to the actual field data. further, these simulated-data are processed using empirical mode decomposit ion and discrete wavelet transform, and features are extracted. these features are then fed to the training of different well-established ml techniques such as support vector machine, random forest and mult i-layer perceptron. to validate this approach, trained ml algorithms are tested using experimental data. the results show more than 87% accuracy with all three algorithms. the performance of the t rained model is evaluated using precision, recall and roc curve. these met ric show the affirmat ive results for the applicability of this approach in gear fault detect ion.