## 1 Introduction  
For data-driven machine learning techniques [1], it plays an important role in the machinery fault diagnosis and prognostics [2; 3]. Recently, deep learning [4] emerges as one that has been progressively adopted to develop health monitoring systems for the machinery. One way of looking at deep learning is as a feature engineering method [5] that automatically extracts features from the collected signals. Propagating these signals from the input layer to layers with less and fewer neurons, the neural network is forced to represent the input data space into a lower dimensional feature space, which, in general, reduces overfitting and increases the accuracy. Deep learning models such as convolutional neural networks [6], deep autoencoders [7], deep belief networks [8], and deep Boltzmann machines [9] have achieved outstanding results in fault diagnosis, and other fields essentially due to the above-described dimensionality reduction effect. In the industrial manipulator field, [PERSON] et al. [10] proposed an algorithm based on the multilayer perceptrons for the manipulator actuator fault detection. [PERSON] et al. [11] proposed a multi-data fusion with an optimized convolutional neural network (CNN) for fault detection of rotating machinery. [PERSON] et al. [12] proposed the convolutional multi-time scale echo state network (ESN) to efficient classification. Three different ESNs with different time scales were used to allow the recurrent neural network to successively refine the features in a similar way of a kernel in a CNN. Due to the fast training of ESN, [PERSON] et al. [13] proposed a deep echo state network optimized by particle swarm optimization to fault diagnosis of a wind turbine gearbox. [PERSON] et al. [14] present an approach with deep Boltzmann machine and multi-grained scanning forest to effectively deal with industrial fault diagnosis. [PERSON] et al. [15] proposed a new deep neural network model based on a deep Boltzmann machine for condition prognosis. [PERSON] et al. [16] proposed a real-time fault diagnosis model using a deep neural network. [PERSON] et al. [17] presented a continuous deep belief network (CDBN) for bearings fault detection. [PERSON] et al. [8] used a deep belief network with an optimized function of Nesterov momentum (NM) for bearing fault diagnosis. [PERSON] et al. [18] presented a new method of a convolutional neural network encoder for feature extraction in tactile robotics. [PERSON] et al. [19] based on the study of how the power flows inside the time synchronous average of the ring gear and a modified statistical parameter for planet gears fault diagnosis. [PERSON] et al. [20] use a Bayesian hierarchical model with utilizing fleet data from multiple assets to perform probabilistic estimation of remaining useful life for civil aerospace gas turbine engines. However, all the above models are critically dependent on a representative, balanced, large enough data set that require data that, more often that not, are hard to obtain [21]. While data from a healthy state are abundant, data from faulty states are rare, sparse, and hardly representative of all possible faults. This could lead to low diagnosis precision in these intelligent fault diagnosis techniques. Without appropriated data, these machine learning methods simply do not have acceptable performance [22], and it is important to consider a method that mitigates such a data shortage. As Robotic manipulators, they are wildly used in the industry as they can be used in a range of tasks such as assembly, painting, or welding [23]. However, the transmission system of a manipulator is prone to faults due to prolonged working periods [24]. Typically, these faults manifest in the connection parts, bearings, gears, or gear shafts. A faulty manipulator will be less precise, less efficient, less productive, and less secure--even though it could be a tricky task to obtain fault data for such precision machinery.