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| Condition Monitoring of Aero-engine Main Bearings Based on Decision Fusion of Vibration and Oil Information |
| Received:August 11, 2024 Revised:September 03, 2024 |
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| DOI：10.7643/issn.1672-9242.2024.09.005 |
| KeyWord:rolling bearing vibration signal oil metal debris decision fusion condition monitoring fuzzy reasoning aero-engine |
| Author | Institution |
| ZHAO Junhao |
School of Aero-engine, Shenyang Aerospace University, Shenyang , China |
| SHA Yundong |
School of Aero-engine, Shenyang Aerospace University, Shenyang , China |
| LUAN Xiaochi |
School of Aero-engine, Shenyang Aerospace University, Shenyang , China |
| LIU Mingguo |
School of Aero-engine, Shenyang Aerospace University, Shenyang , China |
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| Abstract: |
| The work aims to solve the problem of on-line monitoring and fault diagnosis of aero-engine rolling bearing under actual working conditions. Firstly, the effective value was selected as the time domain characteristic parameter, the characteristic energy was proposed as the frequency domain characteristic parameter, and the number of oil metal debris was used as the fusion vibration and oil debris information. The above parameters were fused based on fuzzy inference theory. By selecting membership functions and defining fuzzy inference rules, the vibration signals and oil metal debris information were fused to diagnose bearing faults. The spalling extension test of the aero-engine main bearing was carried out, the vibration and oil debris detection system was installed, the vibration and oil debris information of the bearing in the whole process of spalling was collected synchronously, and the measured data were analyzed by the proposed method.With the fault expansion, the effective value of vibration signal parameters was an overall upward trend. The frequency domain characteristic energy decreased and fluctuated with the increase of fault spread to a certain extent, which was sensitive to early fault diagnosis of the bearing. Oil debris was the important information for bearing fault diagnosis, and its change trend was monotonically increasing. Oil debris information changed significantly in the later stage of bearing fault, which was sensitive to bearing fault diagnosis. The vibration and oil debris information fusion method based on fuzzy reasoning theory can comprehensively analyze the fault characteristics of different signals and effectively distinguish the running state of bearings. |
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