## 1 Introduction
Up to date, intelligent methods (e.g., BPNN) have been extensively investigated and used to diagnose faults in rotating machinery [18; 19]. [PERSON] et al. [17] combined BPNN with wavelet packet decomposition, where the wavelet packet decomposition coefficients were used to extract eight energy features, and the BPNN was used to do recognition with validation accuracy at 92.5%. Recently, deep-learning methods, such as the DBN-WPT combination, have been determined to overcome the obstacles in fault diagnosis in complex machines [20; 21; 22]. Deep architectures have been proven to be more effective fault recognition than shallow architecture. However, in these methods, features still need to be selected manually at first and deep models just function as classifiers. The early vibration signals of gear or bearing fault are often characterized as non-stationary and are well-affected by vibrations from other components in the equipment and transmission path [23]. This way, the beneficial information of signals is often restricted by other noises or even completely overwhelmed. Obtaining useful information from a signal polluted by noise is essential for effective fault diagnosis methods. However, inadequate denoising or over denoising can distort the original signals, thereby resulting in useless machinery fault diagnosis or reduced recognition rate. Therefore, an efficient denoising technique is required before analyzing the signals for the characteristic fault frequency retrieval. If the components of the signal are known, then optimal filters can be employed for denoising [24]. Moreover, numerous methods have been developed and applied to the denoising step, such as wavelet transform (WT), ensemble empirical mode decomposition (EEMD), and undecimated discrete wavelet transform (UDWT), among others. [PERSON] et al. [25] denoised signals with digital wavelet frame (DWF) and performed fault diagnosis based on a stacked autoencoder (SAE). It combined low-level features to form more abstract high-level features to represent data distributed characteristics and obtained accuracy around 99%. [PERSON] et al. [26] also proposed a method that combined UDWT and EMD to complete the denoising and diagnosis progress. The UDWT was used to denoise signals and EMD was used to decompose the signals into a number of Intrinsic Mode Functions (IMFs). Even though EMD is an adaptive signal processing method, it suffers from several shortcomings, such as mode-mixing problem that makes analyses of IMFs difficult and empirical. Some fault related signatures may reside in several IMFs, which make the selection/combination of useful IMFs for machine fault feature extraction difficult. Besides, some combinations of the denoising step with the feature extraction and classification step bring more human intervention, such as the base function or other parameters selection affecting the final performance. To determine a concise and efficient method that can simultaneously denoise the signals while extracting features, a stacked denoising autoencoder (SDAE) based on a deep network is proposed to construct a fault diagnosis system. SDAE, which was first proposed by [PERSON] et al., is a stacked neural network comprising several classical denoising autoencoders (DAs) that are trained not to construct their input but rather denoise an artificially corrupted version of their input [27]. DAs have been previously shown to be competitive to restricted Boltzmann machines, constructive unit of a DBN, and for the unsupervised pre-training of each layer [28]. [PERSON] et al. [29; 30] extended DA with a greedy layer-wise procedure of deep learning algorithm. [PERSON] et al. [31] introduced a general mathematical framework for the study of both linear and non-linear autoencoders, thereby enabling the autoencoder to solve additional types of tasks. Thereafter, SDAE has been extensively used in different types of application [32; 33; 34; 35; 36]. In the field of fault diagnosis, [PERSON] et al. [37] placed a stacked autoencoder (SAE) into fault diagnosis.