Evaluation for Bearing Wear States Based on Online Oil Multi-Parameters Monitoring

Abstract

As bearings are critical components of a mechanical system, it is important to characterize their wear states and evaluate health conditions. In this paper, a novel approach for analyzing the relationship between online oil multi-parameter monitoring samples and bearing wear states has been proposed based on an improved gray k-means clustering model (G-KCM). First, an online monitoring system with multiple sensors for bearings is established, obtaining oil multi-parameter data and vibration signals for bearings through the whole lifetime. Secondly, a gray correlation degree distance matrix is generated using a gray correlation model (GCM) to express the relationship of oil monitoring samples at different times and then a KCM is applied to cluster the matrix. Analysis and experimental results show that there is an obvious correspondence that state changing coincides basically in time between the lubricants’ multi-parameters and the bearings’ wear states. It also has shown that online oil samples with multi-parameters have early wear failure prediction ability for bearings superior to vibration signals. It is expected to realize online oil monitoring and evaluation for bearing health condition and to provide a novel approach for early identification of bearing-related failure modes.

Review of Wind Power Bearing Wear Analysis and Intelligent Lubrication Method Research

“…By studying the effect of lubricating grease debris on the capacitance value, it was learned that the mass and size of the lubricating grease debris showed a positive correlation change with the capacitance value. However, this simulation is only a preliminary experiment, and the sensor can be further applied to wind In the development of lubricant monitoring and sensors, Wang et al [207] designed a mean clustering model (G-KCM) for online monitoring of bearing lubricant parameters and vibration signals learned: lubricant parameter information has a corresponding relationship to bearing wear, and the approximate time of bearing wear change can be inferred from the lubricant parameter information, so that bearing failure or early failure can be effectively avoided. This experiment provides a new method for timely understanding the health state of bearings and avoiding bearing failures.…”

Methods of Condition Monitoring and Fault Detection for Electrical Machines

“…Regarding lubricant conditions, which directly impact the bearing's durability, authors in [117] use ultrasonic sensors that were instrumented on the inner and outer bearing raceways to detect lubricant conditions. In [118], the authors propose an improved grey k-means clustering model for monitoring bearing wear conditions. Finally, authors in [119] propose a fault tree analysis for wear monitoring in wind turbine bearings.…”

Journal Bearing Performance Prediction Using Machine Learning and Octave-Band Signal Analysis of Sound and Vibration Measurements

“…There are several approaches used for condition monitoring and predictive maintenance of journal bearings, such as vibration, noise and acoustic emission monitoring and analyses, focusing on detecting and identifying patterns and trends in the recorded signals, and correlating them with present or upcoming fault conditions [1,2]. Further, lubricating oil and wear debris analyses [3] are commonly used for assessing lubricating oil quality [4], focusing on analysis of the size, shape, quantity and composition of wear particles generated during operation, correlating the findings to the machine condition, and determining the effective wear mechanisms (sliding, rubbing, rolling, abrasion, etc.). Among them, vibration analysis is the most popular in practical mechanical engineering applications, supported by a wide related literature, mainly for roller bearing condition assessment.…”

“…The speed-dependent vibrational behaviour is found to be an effective indicator of surface defects. Additionally, vibration signals have also been used for bearing wear state detection by Wang et al [11] and oil analysis for wear debris detection by Appleby [12]. Šaravanja and Grbešić in [13] highlight that the most important step in the vibrational diagnostics of journal bearings is the choice of measuring points, as well as the choice and mounting of sensors, most of which depend on the accuracy of the test and the results obtained.…”