Simultaneous use of Acoustic Emission Signals and Statistical Analysis to Distinguish between Lubrication Modes in Rolling Element Bearings

The lack of lubricant in bearing surfaces could be considered as the main cause of wear and faults in bearing surfaces. To avoid unexpected failures, special emphasis on adequate evaluation of lubrication mode (lubricated/dry) on the bearing surfaces is demanded. To that end, the proper use of reliable techniques and tools, including sensory information from acoustic emission (AE) signals is among popular methods when real-time condition monitoring evolves. The current work intends to evaluate the sensitivity of AE parameters to different levels of process parameters on the basis of statistical analysis. In this context, rotational speed and radial load were used as the main experimental parameters. Following that, adequacy of a new AE signal parameter for real-time condition monitoring of rolling element bearing is presented. Experimental and statistical results confirmed the great capability of AE signals to differentiate between two types of bearing modes, in particular, dry and lubricated. Signal processing and statistical analysis conducted in this study exhibited that several time series AE parameters, in particular, Std, Max, Mean, and Variance are sensitive to the variation radial load and rotational speed. It was observed that radial load has insignificant effects on computed values of AE parameters from both bearing modes. The statistical analysis revealed that rotational speed (A) has a significant effect on all computed AE parameters from the dry bearing.