## 2 Complete Fault Diagnosis Process  
The complete fault diagnosis process of rolling bearing mainly consists of four steps of data preprocessing, feature extraction, fault diagnosis, and evaluation index. The fault diagnosis process includes the data analysis to the diagnosis results. The classification and comparison experiments are carried out based on effective data, and the evaluation is carried out to prove the effectiveness of the method. This paper designed a way of diagnosis directed on the four major steps, respectively, as shown in Figure 1.  
The specific implementation of the steps is as follows. 1. Firstly, the data is cleaned, and the data dimension is unified by the method of data visualization; then the exponential smoothing method is used to smooth the sequence to eliminate the random error, and the main trend of the time-series is obtained. The adaptive sliding window model based on the short-term prediction ability of the exponential smoothing method and the statistical characteristics of historical data is used to segment the time-series data, and the relationship between the statistical characteristics of the historical data and the prediction error is used to determine the segmentation point. In the process of detecting data and transmitting data by the sensor, it is possible to be interfered to obtain an unrealized outlier point. Therefore, in the process of detecting the segmentation point, it is necessary to add a verification step to determine that the obtained split point is not an outlier generated due to interference. In the verification step, the method of setting the flag is used, that is, when a certain point of the time-series does not satisfy the historical trend, the point is set as a suspicious segmentation point. Then, check the next point, when the next point also does not satisfy the historical trend, the suspect split point is determined as the split point, and the flag bit is set; otherwise, the suspicious segmentation point is determined to be the outlier point, and the flag bit is cleared. 2. The nonlinear and non-stationary data characteristics extraction method based on local frequency is designed, which smartly combines the split point obtained from preprocessed data with the spectrum analysis and constructs the localization feature of data. Then, the definition of local frequency and the construction method of time-frequency-domain to proceed with the feature extraction are finalized. This method overcomes the limitation of HHT, only applicable to describe the narrowband signal, while in the meantime, it makes up the setback of Fourier global frequency, which is only worthy for unlimited fluctuating periodical signals. Figure 1: Fault Diagnosis Process Chart. Aiming at the shortcomings of the AdaBoost algorithm in anti-noise, an Improved AdaBoost Algorithm (IAA) is proposed. The method first clusters the data, filters out the noise data, and then combines AdaBoost for classification experiments which enhances the anti-noise ability and improves the classification accuracy. During the learning of the base classifier, the distribution rate of the sample is continuously calculated, and the samples with excessive sample distribution rate are deleted. The IAA is compared with the current mainstream Back Propagation (BP) neural network, standard SVM, and AdaBoost. The classification results of each classification algorithm are obtained through experiments. 4. To compare the performance of each classification algorithm, three evaluation indexes, namely Detection Rate (DR), False Alarm Rate (FAR), and Classification Rate (CR), are defined. The classification results of BP neural network, standard SVM network, AdaBoost, and IAA are evaluated, respectively. The superiority of the IAA is reflected by comparison. The whole fault diagnosis method is synthesized.