## 1 Introduction
However, for non-steady and high-dimensional classification issues, there are bottlenecks, such as over-fitting due to highly strict training goals and a proneness to fall into a local optimum that restricts the performance improvement of ANN. As a statistical learning theory-based method, SVM has unique advantages in solving nonlinear, high-dimensional pattern recognition problems, especially in cases with two-class discriminating issues and fewer samples [18; 19]. [PERSON] et al. [20] used intrinsic time scale decomposition (ITD) to calculate the Lempel-Ziv complexity of PR components to construct the feature vector as the input of SVM to accomplish the classification of different fault types, which has achieved high prediction accuracy without suffering from the influence of load variations. [PERSON] et al. [21] utilized the sparse representation to extract sparse coefficients of both current signal and vibration signals to input the support vector machine for fault diagnosis, it showed that the method is feasible, accurate and suitable for small sample. However, SVM training is difficult when faced with multi-classification issues and large-scale training samples. But in reality, intelligent diagnosis is always based on online monitoring with inevitably large quantities of samples. If fault superposition and damage degree are considered, it is also inevitable that the number of fault modes increases considerably. Therefore, although these methods can automatically and adaptively recognize faults according to the extracted features, which significantly reduce the influence of manual experience and subjectivity in the recognition process and result, shallow learning method limits the capacity to effectively mine features in high-dimensional and non-steady data. As the latest achievement and research focus in machine learning, deep learning methods are widely used in many fields [22; 23; 24; 25]. Benefiting from their deep structure and the training advantages of layer-by-layer greedy learning, deep learning methods can be used to implement advanced nonlinear transformations and can automatically and adaptively learn high-order abstract features from inputs. Therefore, deep learning in intelligent fault diagnosis has attracted an increasing amount of attention. Convolutional neural networks (CNNs) [26], deep belief networks (DBNs) [27], and recurrent neural networks (RNNs) [28] have been introduced for the fault diagnosis of rotating machinery. As one of the most widely used method, stacked autoencoder [29] has attracted attention in fault diagnosis. [PERSON] et al. [30] proposed stacked-autoencoder-based deep neural network (DNN) for roller bearing and gearbox fault diagnosis with frequency spectra after Fourier transform as inputs. [PERSON] et al. [31] proposed an intelligent fault diagnosis approach that uses DNN based on a stacked denoising autoencoder (SDAE) for bearing diagnosis, and the results indicated that this method can extract representative features from massive unlabeled data and achieve high accuracy. [PERSON] et al. [32] proposed an intelligent fault diagnosis model based on wavelet transform and stacked autoencoder for rolling bearing diagnosis. [PERSON] et al. [33] proposed a hybrid feature pool in combination with stacked sparse autoencoder (SSAE) to effectively diagnose bearing faults of multiple severities. By summarizing the existing researches, it is found that most related work of autoencoder in intelligent diagnosis either applies sparse autoencoder (SAE) or denoising autoencoder (DAE) alone. Although in [34], denoising sparse autoencoder was proposed through introducing both corrupting operation and sparsity constraint into a traditional autoencoder, and was applied in MNIST dataset. There are no researches that combine SAE and DAE, and study the influence of different stack order on diagnosis result.