Rolling bearing state monitoring method based on fusion of multichannel vibration signals with oil debris information

Abstract

Health monitoring of rolling bearings in aviation engines is critical for ensuring flight safety. As aircraft engines evolve toward higher thrust-to-weight ratios, increased loads, enhanced intelligence, and higher rotational speeds, the frequency of bearing failures has markedly increased, posing significant challenges to existing monitoring techniques. In response to the challenge regarding the monitoring and accurately diagnosing faults in the main bearings of gas turbine engines via a single detection method, a method for monitoring the state of rolling bearings is proposed based on the fusion of multichannel vibration signals and oil debris information. First, a weighted fusion model integrates data obtained from multiple vibration sensors. Subsequently, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is used to decompose the fused signal, and components with strong impulsive characteristics are selected based on kurtosis–correlation coefficient screening criteria for reconstruction. As a result, a vibration signal rich in bearing fault characteristic information is acquired. The total effective value is selected as the characteristic time-domain feature parameter, and the feature energy is introduced as the characteristic frequency-domain feature parameter. The fuzzy reasoning theory fuses the total effective value and feature energy for the first time into the vibration information parameter F1 by selecting the membership functions and the definition of fuzzy inference rules based on practical considerations and expert experience. Then, the measured number of oil metal debris is considered the debris information parameter F2. Based on the fuzzy reasoning theory, F1 and F2 undergo a second fusion analysis. Ultimately, the rolling bearing state is monitored, and the bearing faults are diagnosed. Experimental tests on the shedding of aircraft engine main bearings are conducted, where a vibration and oil debris detection system is installed to synchronously collect vibration and oil debris information throughout the bearing operation process. The proposed method is applied to analyze the collected data. The results indicate that the rolling bearing state monitoring method based on the fusion of multichannel vibration signals and oil debris information enables the comprehensive analysis of fault characteristics and the effective assessment of the bearing operation states.