In response to the challenges of monitoring and accurately diagnosing the state of main bearings in aircraft engines using a single detection method, a method for rolling bearing condition monitoring is proposed, integrating multi-channel vibration signals with oil debris particle information. This approach initially utilizes a weighted fusion model for multi-channel vibration information to combine data obtained from multiple vibration sensors. Subsequently, the fused signal is decomposed using CEEMDAN, and components with strong impact characteristics are selected based on kurtosis-correlation coefficient filtering criteria, leading to the reconstruction of a vibration signal rich in bearing fault characteristic information. Time-domain features, using the total effective value, and frequencydomain features, employing feature energy, are then extracted as characteristic parameters. Through the selection of membership functions and the definition of fuzzy inference rules based on practical considerations and expert experience, fuzzy inference theory is applied to fuse the total effective value and feature energy into the first-level fused vibration information parameter, denoted as F1. The obtained oil metal debris particle count is utilized as the information parameter F2 for debris, which is further analyzed through a secondlevel fusion using fuzzy inference theory. Finally, the rolling bearing status is monitored, and bearing faults are diagnosed. Experimental tests involving the shedding and expansion of main bearing debris in aircraft engines were conducted. A detection system was installed to simultaneously collect vibration and oil debris particle information throughout the entire bearing shedding process. The proposed method was applied to analyze the collected data. Results indicate that the multi-channel vibration signal and oil debris particle information fusion method for rolling bearing condition monitoring enables comprehensive analysis of fault characteristics and effective discrimination of bearing operational states.