AI Summary & Key Takeaways
AI InsightIt has been found that the proposed approach is quite beneficial for not only detection, but also classification of pitting fault levels in helical gearboxes.
📌 3 Key Takeaways
Abstract
Gearboxes that are frequently used as power transmission elements are experienced various faults over time. The pitting fault, which is one of these faults, is usually caused by insufficient lubrication and overloading. Detecting pitting fault and identifying different pitting levels are challenging subjects in gear fault detection. This study aims to propose a method to classify the different levels of pitting fault in helical gearbox. It is known that gear defects illustrate themselves in vibration signal at gear mesh frequency (GMF) and its harmonics. As the severity of faults on the tooth surface grows, the amplitude of these frequencies usually increases in the frequency spectrum. Frequency component based statistical analysis (FCSA) method is utilized to obtain stronger indicators for fault classification. In this study, frequency component based statistical analysis calculates the mean, standard deviation, RMS and Kurtosis values of narrowband gear vibrations obtained around the GMF and its harmonics in order to detect these increases in the frequency spectrum. Moreover, these statistical parameters are then used as an input for training and testing of artificial neural network (ANN) for classification of pitting faults. Furthermore, the pitting fault is detected and different pitting levels are classified. It has been found that the proposed approach is quite beneficial for not only detection, but also classification of pitting fault levels in helical gearboxes.