Lubrication performance plays a key role in the lifetime of bearings. Online quantitative monitoring of the impurity contents of lubricants is an effective way to evaluate the performance of lubrication conditions. However, mainstream vibration monitoring techniques are often incapable of providing information on lubrication contamination especially for low-speed and high-load cases in which the dynamic interaction is insignificant. In this paper, an acoustic emission (AE) method is developed to achieve quantitative evaluation of the impurity content of lubrication greases, which are commonly used as lubricants for low-speed and heavy-duty bearings. In particular, a Peak-Hold-Down-Sample algorithm is proposed to compressively sample the large volume AE data acquired at the rate of several megahertz. Both simulations and experiments show that Peak-Hold-Down-Sampled AE data contain information about the deferent levels of impurities. Therefore, the proposed AE approach can be used to monitor lubrication performance in extreme operations.
Quantitative evaluation of the impurity content of grease for low-speed heavy-duty bearing using an acoustic emission technique
