Therefore, the robust data processing method is very important.. Vibration is an important performance index of rolling bearings, which comprehensively reflects the manufacturing, installation, lubrication and other factors of the bearing, and affects the dynamic characteristics, life and reliability of the bearing. According to the data robustness principle of modern statistics, the robustness of data is one of the most basic conditions for data analysis-the more robust the data, the more reliable the evaluation results obtained. Reasonable evaluation of the vibration performance variation of rolling bearing bearings has very important application value.

Assessment. It can discover the hidden danger of bearing failure in time and take measures in advance to avoid major safety accidents. These properties have an important impact on the operating performance of the mechanical system. The performance variation of rolling bearings is characterized by the variation rate, which is the ratio of the number of variation data to the total number of data. This chapter organically integrates these two optimal estimates and complements each other's advantages. It is difficult for actual engineering technical problems Mechanical roller bearings to meet this condition and lacks practicality. For this reason, based on the principle of data robustness in modern statistics, this chapter proposes a method for evaluating the variation of the vibration performance of rolling bearings to detect the performance degradation of rolling bearings during service.

The evaluation method of rolling bearing performance variation proposed in this chapter does not need to assume the performance degradation model, distribution law, probability density function and threshold value in advance. In this chapter, the variation of rolling bearing performance refers to the degree of change and degradation of rolling bearing performance during the experiment and service period; the median refers to the absolute value of the rolling bearing vibration data, and then sort it in ascending order to obtain the absolute value sorting sequence , And then according to the median of the absolute value sorting sequence obtained by statistics; the average refers to the average of the improved data sequence obtained according to the Huber M estimation principle; the closeness of the average to the median is the average of the improved data sequence Characterization of the absolute difference from the median of the absolute value sorted sequence.

After robust processing of the actual measured bearing vibration data, the overall intrinsic interval is directly obtained, and then the performance degradation is implemented. Analysis method of rolling bearing vibration characteristic parameters based on phase space. The variation data refers to the data that is not in the overall intrinsic interval; the overall intrinsic interval is the intrinsic reflection of the data and is robust The data distribution interval after processing. In the process of data robustness, the median is the most robust estimate, and the average is not a robust estimate, because the average is easily affected by outliers.

It proposes a robust data processing method that can reflect the location characteristics of the data as well as the overall data situation, and use the liver to evaluate the variability of rolling bearing vibration data. Huber M estimation and median estimation are the two optimal estimations under the principle of minimizing and maximizing data robustly in modern statistics. The evaluation method of the vibration performance variation of rolling bearings proposed in this chapter includes the variation rate and the overall intrinsic interval. The closer the average is to the median, the more robust the data.

In this chapter, the closeness between the average value and the median is used as a judgment element to detect the performance degradation of rolling bearings during service, which is a robust estimation. These methods need to assume in advance specific performance degradation models, distribution laws, probability density functions and thresholds, and do not involve the robustness of rolling bearing vibration data. The median estimate is only a robust data that can reflect the location characteristics of the data, but it cannot reflect the overall data trend.he performance of rolling bearings mainly includes vibration, noise, friction torque, temperature rise, rotation accuracy, etc.

Huber M estimation can reflect the overall data situation, has a critical value, and is centered on the data zero. At present, the methods to study the vibration performance of rolling bearings mainly include time-domain characteristics of bearing vibration signal data and neural network method, spectral analysis method of bearing vibration signal, gray bootstrap method of bearing vibration data, and bearing vibration characteristic analysis method based on Hilbert-Huang. It is an odd function about the symmetry of the zero center

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