An intelligent detection system development for local faults in a ball bearing

Abstract

Local faults can be produced in ball bearings during their manufacturing process. An efficient, fast and accurate local fault detection method can help improve the quality of ball bearings. To overcome this problem, an intelligent detection system for a ball bearing with the local faults is developed based on the NI LabVIEW software. This system includes the determination of bearing fault parameters, signal acquisition, envelope analysis, time-domain parameter analysis and bearing fault status modules. In this system, the frequency-domain feature method is based on the envelope demodulation analysis, the effective statistical indexes, and the Pearson correlation coefficient. The frequency-domain feature method is used to determine the threshold range for each fault level in the system. This system can in turn be used to determine the fault location and sizes for the ball bearings. A case study for the calculation and analysis for the frequency and time-domain acceleration is presented to predict the location and size of the local faults in a ball bearing. The test data from the Case Western Reserve University Bearing Data Center is used to verify the developed intelligent detection system for local faults in the ball bearing. The results show that the proposed detection system can be used to detect the local fault in the ball bearings.