Review of Condition Monitoring of Rolling Element Bearing Using Vibration Analysis and Other Techniques

Background Different types of machines having rotary component are linked together in process industries, to perform the process of manufacturing. The failure of any single machine rotary component in the process can result in loss of money per downtime hour. To continue the working of the machines, it is necessary to monitor the health of the machine during its operation. Bearing failure is considered to be one of the major causes of breakdown in different rotating machines operating in industry at high and low speeds. Purpose Vibration in machines is generated due to defective bearings. During use, the condition of the bearings change, hence the vibrations also change and its characteristic basically depends on the cause. Hence, this characteristic feature of the bearing makes it suitable for vibration monitoring and other monitoring techniques. Methods This article presents the brief review of recent trends in research on bearing defects, sources of vibration and vibration measurement techniques in the time domain, frequency domain and time frequency domain. Results The study reveals that, the envelope analysis method and time-frequency analysis technique are able to detect the bearing fault effectively. The wavelet analysis technique along with artificial neural network and fuzzy logic is also found to be the most effective techniques for fault analysis in rolling element bearing. Conclusion Finally, it is concluded that vibration analysis technique along with other condition monitoring techniques gives better results in the fault diagnostics and condition monitoring of rolling element bearing.