| Citation: | LUAN Xiaochi, BAI Tian, ZHAO Junhao, et al. Integration of vibration and lubricating metal particle information for condition monitoring of aircraft engine main bearings[J]. Journal of Aerospace Power, 2025, 40（5）：20240542 doi: 10.13224/j.cnki.jasp.20240542
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To address the challenge of online monitoring and accurate fault diagnosis of the main bearings in aircraft engines using a single detection method, a state monitoring approach based on the fusion of vibration signals and oil debris information was proposed. First, the root mean square value was selected as the time-domain feature parameter, feature energy was defined as the frequency-domain feature parameter, and the number of metal debris particles in the oil was used as the oil debris information. Based on fuzzy inference theory, these parameters were fused by selecting membership functions and defining fuzzy inference rules to perform fusion analysis and fault diagnosis of the bearings using vibration signals and oil debris information. The results indicated that after operating 1 hour, the calculated bearing condition value was 0.18, which fell within the range of 0 to 0.35, signifying that the bearing was in good condition. After medium-term operation of 9 hours, the bearing condition value was 0.5, within the range of 0.35 to 0.65, indicating a suboptimal bearing state. Running towards the end of its operation of 18 hours, the calculated bearing condition value was 0.82, within the range of 0.65 to 1, suggesting severe bearing failure. The proposed information fusion method can effectively monitor the operational status of the bearing and can provide an effective means for the condition monitoring of the main bearing in an aircraft engine.
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