A Novel Fault Diagnosis Algorithm for Rolling Bearings Based on One-Dimensional Convolutional Neural Network and INPSO-SVM  
[PERSON]  
1 School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai 264209, China; [EMAIL_ADDRESS] (Y.S. ); [EMAIL_ADDRESS] (C.Z. ); [EMAIL_ADDRESS] (Y.S. ); [EMAIL_ADDRESS] (Q.X.) [PERSON]  
2 School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai 264209, China; [EMAIL_ADDRESS] (Y.S. ); [EMAIL_ADDRESS] (C.Z. ); [EMAIL_ADDRESS] (Y.S. ); [EMAIL_ADDRESS] (Q.X.) [PERSON]  
1 School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai 264209, China; [EMAIL_ADDRESS] (Y.S. ); [EMAIL_ADDRESS] (C.Z. ); [EMAIL_ADDRESS] (Y. Therefore, it is necessary to find an effective feature extraction method to obtain intrinsic features of the signals. Wavelet packet transformation (WPT) [6], empirical mode decomposition (EMD) [7] and fast Fourier transformation (FFT) [8] are three kinds of widely applied feature extraction methods in the vibration signal processing field. After feature extraction, principal component analysis (PCA) [9] and independent component analysis (ICA) [10] methods usually are selected to remove the low sensitivity and useless data from the extracted features. With the feature vectors selected and extracted from raw signals, state classification is accomplished by K nearest neighbor (KNN) [11], support vector machine (SVM) [12] and other machine learning algorithms and statistical frameworks [13; 14]. The application of traditional fault diagnosis algorithms has achieved certain results, but there are still some shortcomings, such as the feature selection excessively relies on the experience and prior knowledge of the engineers [15], furthermore, it is very subjective and time-consuming [16]. At the same time, traditional machine learning models are faced with dimensional disaster, overfitting and other problems, and the shallow models are difficult to represent the complex mapping relationships between signal samples and health status [17]. As [PERSON] et al. [18] proposed the method of layer-by-layer unsupervised training combined with supervised fine-tuning, deep learning (DL) theory has become a great breakthrough in the field of machine learning and artificial intelligence [16]. Compared with traditional intelligent fault diagnosis methods, the deep model can learn representative invariable features and complex nonlinear relationships from the raw data automatically. As a classical deep learning model, the convolutional neural network (CNN) has a strong feature learning ability with its unique convolution structure, which has achieved great success in the field of image recognition and has been widely applied in fault diagnosis. [PERSON] et al. [19] proposed a CNN model used for intelligent fault diagnosis of gearboxes with manually extracted features. [PERSON] et al. [20] proposed a CNN model for rotating machinery conditions recognition whose inputs are two-dimensional matrix transformed by time domain signals. [PERSON] et al. [21] adopted the data processed by the discrete Fourier transform method as the inputs of a three-layer CNN model for fault diagnosis. Many research results show that the CNN network has a strong ability for learning sensitivity and robustness. However, how to preprocess the raw data efficiently, e.g., the transformation process of one-dimensional vibration signals into two-dimensional matrices for CNN inputs, is a thought-provoking question. To the best of our knowledge there is no complete theory to guarantee the transformation is reasonable. In this paper, the proposed 1 DCNN network avoids time-consuming data preprocessing and extracts features more accurately by carrying out automatic adaptive learning on one-dimensional raw vibration signals. In the field of image processing, the fully connected layer of a CNN network generally adopts softmax function to classify, which can achieve a good classification accuracy.