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.
Highlights
In the 21st century depends on human technological breakthroughs, gave birth to the history of the fourth industrial revolution
The characteristics of the vibration signal are analyzed by the axis trajectory technique, and the characteristic parameters are extracted by using the fractal theory
The statistical features are used to excavate the hidden faults in the vibration signal, and the most suitable indicators are screened for histogram statistics
Summary
In the 21st century depends on human technological breakthroughs, gave birth to the history of the fourth industrial revolution. In the precision machining industry, if the machine can not effectively detect the abnormal situation, long-term will deepen the seriousness of mechanical failure, and lead to production line shut down, shortened equipment life and operator safety threats and other issues. In the course of research on rotating machines, Chang et al [1,2,3,4,5,6,7,8,9,10] used in-depth analysis of rotating machinery, using different techniques [1], such as electrical detection, vibration detection and partial discharge detection. The use of electrical detection method [4], partial discharge detection method [5] to analyze the impact of rotating
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
