Fault prognosis of planetary gearbox using acoustic emission and genetic algorithm: a case study

International audienceOne of the most important aspects of machine fault prognosis is the selection of sensors and features to representthe degradation process of a faulty component. Several approaches in the literature have used features extractedfrom vibration signals to estimate the future condition based on time series forecasting. Another technologythat has been used increasingly for this task are the acoustic emission (AE) sensors, which have frequencymeasurement ranges much higher than vibration sensors. On gearboxes some studies have shown that the AEtechnology can be used effectively for fault diagnosis, but its use for fault prognosis is still a relative new fieldof research that offers encouraging opportunities. One downside of the application of the AE technology ingearboxes is the strong dependence of the AE on the oil temperature, which may lead to difficulties during theforecasting of an AE-based feature. Thus, in this study a novel feature based on a relative counting of the AEbursts is proposed and tested with data from a planetary gearbox with a ring gear fault. The proposed featurereduces the influence of the temperature on the generation of AE when it is compared to the counting basedon a fixed amplitude threshold. Therefore, it can then be more suitable for fault prognosis than traditional AEcounting. In this case study the forecasting of the proposed feature is carried out using an artificial neuralnetwork (ANN), whose hyperparameters were selected using a genetic algorithm. The results are promisingand constitute a basis for further researc