## 1 Introduction
Therefore, the monitoring of the manipulator health condition with limited data sources is of paramount interest. For mitigating this, some fault diagnosis works resorted to the SMOTE. SMOTE stands for Synthetic Minority Oversampling Technique and aims at compensating for the data unbalance of a given class by increasing the number of samples in that class. Roughly, it creates a new synthetic sample by interpolating two existing similar samples of the same class, the new sample having the same class of the two originating samples [25]. SMOTE is a popular method but sometimes, by increasing the number of samples, it will also increase the overlapping between classes. An alternative approach to cope with the unbalanced data set problem is to resort to deep learning and in particular to generative adversarial networks (GAN). The idea is to use the generative model of a GAN to generate enough samples for effective training of the diagnoser. GAN was first proposed by [PERSON] [26] and consists of two adversarial models (neural networks): a generator and a discriminator. The learning process can be described as a min-max game. The generator produces synthetic examples while the discriminator tries to decide whether the current input is either a real or a synthetic example. Both models improve their performance simultaneously up to a Nash equilibrium using a gradient-based optimization technique. The number of GAN applications has been steadily increasing. A sample of recent applications is offered next. In the image processing field, [PERSON] et al. [27] presented SRGAN, a generative adversarial network for image super-resolution. [PERSON] et al. [28] demonstratedthat the conditional adversarial network is a promising approach for image-to-image translation tasks. Based on the above comment, [PERSON] et al. [29] presented an approach for learning to translate an image from a source domain to a target domain in the absence of paired examples. [PERSON] et al. [30] develop an auxiliary classifier GAN (ACCGAN) to learn from mechanical sensor signals and generate realistic one-dimensional raw data. [PERSON] et al. [31] proposed a novel fault detection method for 3D printers using GAN which consider only normal condition signals with outstanding performance. [PERSON] et al. [32] used a GAN for an unbalanced data driven fault diagnosis of rolling bearings. In addition, for rolling bearings, [PERSON] et al. [33] proposed a novel anomaly detection approach based on GAN with only health data. [PERSON] et al. [34] applied a GAN for the feature space learning in fault diagnosis of 3D printers using only one sample of each faulty state. [PERSON] et al. [35] proposed a method based on a conditional variational auto-encoder and a generative adversarial network for unbalanced fault diagnosis of a planetary gearbox. To the best of our knowledge, no work reported model based generation of synthetic samples for fault diagnosis of robotic manipulators. Hence, the main contributions of our work are: (1) The application of GAN to generate synthetic examples (signals) representing fault states for mitigating the presence of an unbalanced data set in a fault diagnosis task of an industrial robotic manipulator. More concretely, GAN generates a synthetic wavelet packet transform based feature of a vibrational signal as acquired by an accelerometer; (2) A comprehensive study taking into account six different scenarios for mitigating the unbalanced data, including classical under and oversampling (SMOTE) methods as well as for assessing the effect of factors such as generator selection, the number of training examples in each class, data shuffling in training data, the distribution used for sampling input random data and initial conditions. The rest of this paper is organized as follows. The proposed GAN based fault diagnosis scheme is specified in Section 2. The manipulator experiment was presented in Section 3. The fault diagnosis of the manipulator was analyzed in Section 4 to validate the experiment result.