## 5 Conclusions  
Robotic manipulators are wildly used in the industry and their maintenance and monitoring systems are resorting more and more to data intensive machine learning methods. Methods such as multilayer perceptrons, convolutional neural network, echo state networks, or deep Boltzmann machines have all been used for such endeavors. However, all of these methods rely on a representative, balanced, and large enough training set, which, due to the very nature of (some) faults, is very hard to collect from the equipment. Motivated by the recent success of generative adversarial network (GAN), in this work, we have exploited, for the first time, this type of generative model as an oversampling method for fault classification in an industrial robotic manipulator. A comprehensive empirical analysis was performed taking into account six different scenarios for mitigating the unbalanced data, including classical under and oversampling (SMOTE) methods. In all of these, a wavelet packet transform combined with GAN is used for feature generation while a random forest is used for fault classification. Studies were also conducted for assessing the sensibility of aspects such as generator selection, the number of training examples in each class, training data shuffling, the distribution used for sampling input random data, and initial conditions. The main conclusion is that it was possible to increase the performance of the fault diagnoser for an industrial robotic manipulator for any of GAN based models over classical undersampling and oversampling (SMOTE) methods. This is accomplished at the expense of a much higher design and computational effort. Training a GAN is not an easy task due to the model collapse and other factors, and it is certainly a quite time-consuming process. After training a GAN for each fault, one will have a set of generators able to produce as much synthetic data as required in an efficient way though. Within the GAN based models, those that keep track of the best current generator during training yielded the best results. No statistically significant difference was observed between the scenario that uses exclusively synthetic data for the faulty states and the scenario that uses the available real data for such states. This is yet another piece of evidence on the quality of the obtained GAN generators. In many cases like prognostics and health management (PHM), enough data can effectively improve the fault monitoring capability of the industrial system. However, it can not completely be executed due to a lack of faulty data. GAN is an efficient tool to get rid of the limitation of data imbalance state, which can enhance the monitoring capability in PHM. Therefore, this approach can provide a data background for PHM. However, this approach still has limitations, one is that this model can only learn the data distribution from a limited faulty data source while there are many kinds of faulty data in the industrial system. For the new faulty data, they need to be sent to this model to learn the new distribution again to generate enough data that will bring time cost for training. Another one is that this approach is trained by sending one single faulty class as an input to GAN in order to obtain a generator. This means we need to train several GAN models for several faulty classes, which is computationally demanding and time consuming. Writing--original draft preparation, and investigation, Z.P. ; methodology, D.C.; writing--review and supervision, J.V.d.O. ; funding acquisition, C.L. ; investigation, R.-V.S. ; investigation, M.C. All authors have read and agreed to the published version of the manuscript. This work is financed by national funds through FCT--Foundation for Science and Technology, I.P., through IDMEC, under LAETA, project UIDB/50022/2020. This work was supported in part by the National Natural Science Foundation of China under Grant 51775112, the Natural Science Foundation of Chongqing cstc2019 jcyj-zdxmx003, the CTBU Project under Grant KFJJ2019060.