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

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.