Contributions to the diagnosis and prognosis of ring gear faults of planetary gearboxes using acoustic emissions

Despite the progress made in the last decades in the field of machine condition monitoring, there are still cases where the current state of the art is not enough and new technologies and advanced analysis methods are required to prevent failures. One example are planetary gearboxes (PGs), which are one of the main powertrain components of heavy machinery such as off-highway trucks, electric rope shovels, helicopters and wind turbines. Although those systems are most of the time equipped with vibration, temperature and other sensors to detect faults in mechanical components, these technologies might not be able to perform well under certain circumstances. Therefore, the applied investigation on new monitoring technologies and methods in the field of machine health management is a necessary step. In this work the use of the acoustic emission (AE) technology for the fault diagnosis and prognosis of PGs is addressed. Different signal processing methods are presented and their potential for the analysis of AE signals is discussed. They include methods in the time domain, frequency domain and time-frequency domain such as calculation of statistical features, detection of AE bursts, envelope analysis, empirical mode decomposition (EMD), cyclostationarity and the wavelet transform (WT). The methods are tested with experimental data measured on different PGs, including not only laboratory measurements but also measurements on wind turbine gearboxes in field. Two types of ring gear faults are analyzed, which are worn and cracked teeth. Regarding the fault diagnosis of a worn tooth, the results indicated that it can be detected by the analysis of the envelope spectrum in account of the amplitude modulations that it produces in the measured AE signals. However, due to the complexity of the AE signals those amplitude modulations can be only properly revealed by the use of complementary signal processing techniques to enhance the signal-to-noise ratio. A case study regarding the prognosis of this fault is also presented. Here, a novel feature based on a relative counting of the AE bursts is proposed and its forecasting is carried out by means of a genetically-optimized artificial neural network (ANN).Regarding the fault diagnosis of a cracked tooth, no clear results were obtained with the same approach used for the worn tooth. For this case, a concept based on the the characterization of the AE bursts with respect to their shape and main frequency proved to be successful. An important aspect of the proposed characterization methodology was the calculation of the main frequency of the bursts. Here, due to the overlapping of normal bursts originated from teeth contact and abnormal bursts originated from crack growth, only the wavelet packet decomposition (WPD) could achieve appropriate time and frequency resolutions to perform this task.The results obtained in this research work constitute the basis for the analysis of AE signals to detect faults in PGs. At the end of the work, recommendations for the development of a reliable condition monitoring system based on AE signals are also given