Detecting insufficient lubrication in rolling bearings, using a low cost MEMS microphone to measure vibrations

This paper proposes a method for detecting insufficient lubrication in rolling bearings, by utilising a low cost MEMS ultrasonic microphone. We run a set of grease lubricated bearings under load, alternating between good and insufficient lubrication conditions by adjusting speed and temperature, over an 18 day experiment, followed by a short validation experiment on a different set of bearings on a secondary test rig. Vibration signals are recorded from an ultrasonic microphone, using a purpose built prototype sensor, then band-pass and high-pass filtered and subsequently analysed using statistical time-domain features. The viscosity ratio Kappa of the lubricant is used as a metric for quality of oil-film formation, and is calculated throughout the experiment from rotating speed and temperature. The results are compared with the predictions from a linear regression model and a simple Neural Network trained on ensembles of three and ten selected features. The three Hjorth’s Parameters activity, mobility and complexity calculated on high-pass filtered microphone signals from the experiments show a good correlation with the viscosity ratio Kappa. When combining these, using a Linear Regression model or a simple two layer Neural Network model, we are able to predict the viscosity ratio with good precision. The proposed method may be used to assess the quality of oil film formation in bearings during operation under varying speed and temperature conditions.