Intelligent Defect Diagnosis of Spiral Bevel Gears Under Different Operating Conditions Using ANN and KNN Classifiers

Abstract

Spiral bevel gears are important part of many mechanical transmission systems and are known for their smooth operation and strong load carrying capacity. This type of gear has a high contact ratio, which makes it very difficult to diagnose even serious defects. Therefore, spiral bevel gears have rarely been used as a reference for defect diagnosis techniques. To overcome these challenges, artificial intelligence (AI) techniques are used in this research to diagnose defects in spiral bevel gears. Although Al techniques in the field of fault diagnosis have been very successful, however, these methods largely use the assumption that the training and test data come from the same operating conditions. However, when the operating conditions in which the trained model is deployed for predictions, differ from the operating conditions in which the model was trained, the performance of these approaches might be significantly reduced. Outside the laboratory, in real-world applications, operating conditions significantly vary, and it is difficult to obtain data for all potential operating conditions. To overcome this limitation and to make AI techniques suitable for diagnosing spiral bevel gear faults under different operating conditions, an effort is made to find fault distinguishing features, which are lesser sensitive to operating conditions. Artificial neural network (ANN) and K-nearest neighbors (KNN) are used as classifiers for fault detection. Performance comparison between both classifiers is made to determine their individual capability and suitability for diagnosing defects of spiral bevel gears under different operating conditions.