A histogram statistical method for the detection of localized faults in deep groove ball bearing

Abstract

Abstract. This study aims to use the histogram statistical method to establish a deep groove ball bearing fault diagnosis strategy. First, statistical indicators are used to excavate the fault characteristics buried in the vibration signal, and use the histogram to define the characteristic area for fault diagnosis. The results show that the indicators 1, 3, 6 have better statistical differences. Based on this, the accuracy of pattern recognition for all test data is 100 %. Finally, the statistical significance of ball damage was significant, and the results showed high correlation (56~73 %). The correlation between inner race damage model was 49~57 % and healthy model was 52 %. As the inner race damage and health model in the statistical sense, there are some similar, so there is a relatively high correlation. In the future research work, it will be committed to mining more representative indicators to enhance the relevance of abnormal characteristics.

Development of cavitation detection methodology for hydraulic turbomachines using experimental and numerical tools

“…This observation is important, because it adds a second aspect on the conclusions made in section 6.2.1, by characterising statistically the impulses generated during the two-phase flow development, as non-Gaussian with respect to the density frequency of both the raw noise and vibration data. However, the aforementioned nature of the raw measurements can not be directly related with cavitation, as similar statistical characteristics may appear due to various faults related with rotating machinery (Al Hashmi, 2009;Lin, 2017). As a result, additional work is made for the identification of the statistical moments' effect in the non-Gaussian nature of the present data.…”