There are a lot of interference factors in the operating environment of machinery, which makes it ineffective to use traditional detection methods to judge the fault location and type of fault of the machinery, and even misjudgment of the fault location and type may occur. In order to solve these problems, this paper proposes a bearing fault diagnosis method based on wavelet denoising and machine learning. We use sensors to detect the operating conditions of rolling bearings under different working conditions to obtain datasets of different types of bearing failures. On the basis of using the wavelet denoising algorithm to reduce noise, we comprehensively evaluated five machine learning models, including K-means clustering, decision tree, random forest, and support vector machine to classify bearing faults and compare their results. By designing the fault classification evaluation prediction criteria, the following conclusions are drawn. The model proposed in this paper is significantly better than other traditional diagnostic models for bearing faults. In order to solve the problem of weak signal strength and background noise interference, this paper selects a better noise reduction algorithm under different quantitative evaluation indicators for wavelet denoising, which can better restore the true characteristics of the fault signal. Using unsupervised learning and supervised machine learning classification algorithms, the evaluation indicators before and after denoising are compared to make the classification results more accurate and reliable. This article will help researchers to intelligently diagnose the faults of rolling bearing equipment in rotating machinery.

In order to quickly and timely analyze the airborne and controllers data of domestic ARJ21 (Advanced Regional Jet for 21st Century) aircraft and find out the existing flight problems and potential safety hazards, this paper proposes a DBSCAN (density-based spatial clustering of applications with noise) clustering analysis method for aircraft airborne and controllers data outlier detection to evaluate and monitor the flight status. The flight QAR (quick access recorder) data stored in the aircraft rapid data recorder are input to the DBSCAN clustering analysis algorithm to detect the flight parameters that are different from the normal range. Compared with the traditional airborne data analysis method, this method can realize the real-time analysis and prediction of data, improve the efficiency of data analysis, and find the potential security risks according to the analysis results and deal with them in time. In this paper, 1,102 ARJ21 aircraft operation data are used to test. The results show that the DBSCAN clustering anomaly data detection method based on density is fast and accurate in detecting the continuous parameters recorded in the flight process, and the display results are easy to analyze, which can predict the potential safety problems in time. The outliers detected by this method can provide support for the controller to detect the outliers and related flight risks in daily flights.

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