## 1 Introduction  
With advanced manufacturing industry (AMI) being attached an increasing importance by most countries in today's world, effective machine health assessment theory is undergoing an unprecedented revolution. Evaluating and monitoring the performances of some pivotal components, such as gears or bearings can detect the degradation or faults and correct them before machine breakdown occurs [1]. According to References [2, 3, 4, 5], signal processing methods involving time-frequency entropy, wavelet transform, etc., are most popular among the existing works for health assessment. As modern mechanical structure becomes increasingly complex, the vibration signal that characterizes the running state of machine needs to be analyzed more accurately. However, the incipient fault features of target machines is usually so weak that it is always submerged in the strong noise environment and difficult to extract. Therefore, more effective methods that can extract representative features from volatile machine running conditions and provide precise evaluation results are urgently needed. A number of artificial intelligence techniques have already been applied for distinguishing machinery health conditions, for example, [PERSON] et al. [6] proposed a novel approach of analog circuit fault diagnosis by using a support vector machine (SVM) classifier. A kind of hidden Markov model (HMM)-driven robust probabilistic principal component analyzer was created by [PERSON] et al. [7] for dynamic process fault classification. In addition, [PERSON] and [PERSON] [8] adopted back-propagationneural network (BPNN) to assess on-line bearing performance degradation. More recently, diverse novel machine learning algorithms such as deep neural networks (DNNs) [9], deep belief networks (DBNs) [10], and convolutional neural networks (CNNs) [11] have been effectively applied in the related field. Additionally, a new hierarchical network that stacks DBNs layer by layer was proposed by [PERSON] et al. [12] to assess mechanical systems. However, there is currently no proven mature method can be effectively employed in the establishment of deep neural network models. In practice, researchers have to continually change parameters and experiment multiple times to build a model structure, of which the instability and uncertainty have made the development and application of machine learning models limited, and even questioned. This paper proposes to adopt the particle swarm optimization (PSO) algorithm [13] to optimize the model parameters of DBN for machine condition assessment, which can reduce the complexity of DNN modeling. Nowadays, in addition to time and frequency analysis and other traditional methods, some novel analysis techniques such as short time Fourier transform (STFT) [14], Wigner-Ville Distribution (WVD) [15], and wavelet packet transform (WPT) [16] have also been effectively applied to extract vibration signal features. Feature fusion reduction is the process to produce new and more sensitive features through a series of transformations or combinations of the original input feature sets. Compared with feature selection, physical meaning of the new features that acquired after fusion reduction is obviously different from that of original ones and is difficult for understanding. However, since all of the features are involved in the transformation, feature fusion reduction is capable of retaining most of the useful signal classification information by projecting the feature set from high dimensional feature space to low dimensional feature space directly.