Vibration Signals Based Bearing Defects Identification Through Online Monitoring Using LABVIEW

Abstract

Rolling element bearing is one of the important components in rotary machines. Although a significant quantum of work has been done on bearing defect monitoring, estimation of defect size in bearing elements is still a challenge. Vibration signals resulting from rolling element bearing defects, present a rich content of physical information, the appropriate analysis of which can lead to the clear identification of the nature of the fault. The proposed research work is examined under laboratorial set-up keeping rotating speed and or load variation remains unchanged. Hence this paper represents the online monitoring approach of identifying outer, inner race and ball defects in the ball bearing based on the location, count and size of the defects by incorporating PLC with LABVIEW platform. A monitoring tool acquires experimental data from a bearing vibration control test rig. An accelerometer captures the signal from the bearing outer ring then it is processed using PCI-4451 National Instruments data acquisition board and LabVIEW software. To analyze the peak value of the vibration signals during faults, the following 0.5, 1-and 2-mm defect size were undertaken. Through real-time experimentation the minimum and maximum range of vibration amplitude values were observed by varying the defect counts as single and multi-nature basis on three zones like inner, outer race and ball of the bearings. Its experimental numerical results reveal that of ball bearing without defects holds values between 10 mV to 16 mV and it also confirms that the ball defects hold higher amplitude vibration on comparison with defects on the inner and outer race vibration signals.

Discrete Wavelet Design for Bearing Fault Diagnosis Using Particle Swarm Optimization

“…However, time and frequency domain features have been shown to be relatively inappropriate for nonstationary signals which can be better processed using timefrequency domain techniques able to provide simultaneously, both time-domain and frequency-domain information [6]. To deal with non-stationary signals, including mechanical and bearing faulty signals, many time-frequency domains techniques have been used, such as the short-time Fourier transform (STFT), the Wigner-Ville distribution (WVD), the empirical mode decomposition (EMD), and the wavelet transform (WT) [7][8][9][10][11]. More specifically, the WT based approach, due to its effectiveness, has been widely and successfully applied in monitoring mechanical diagnosis problems and bearing fault detection.…”