| ZHOU Liang 1 ， WANG Hua-wei 1 ， XU Shan-shan 2 ， WANG Qing-wei 1 ， WANG Ying 1.Aeroengine Fault Diagnosis Based on Cluster Analysis of Lubricant Wear Particles[J].航空发动机,2024,50(6):113-119 |
| Aeroengine Fault Diagnosis Based on Cluster Analysis of Lubricant Wear Particles |
| DOI： |
| Key Words:identification clustering fault diagnosis condition monitoring particle swarm optimization support vector machine aeroengine |
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| Abstract:In order to effectively analyze the lubricating oil status and realize the wear status monitoring of key components of
aeroengine based on lubricating oil status analysis, aeroengine fault diagnosis research based on lubricating oil abrasive particle clustering
analysis was carried out. The principal component analysis algorithm is used to extract the key feature parameters of lubricating oil abrasive
particles. Combined with the key feature parameters, the support vector machine model is used to perform cluster identification of lubricat?
ing oil abrasive particles. By analyzing the improved particle swarm algorithm, the support vector machine model is optimized using the
adaptive weight particle swarm algorithm and the asynchronous shrinkage factor particle swarm algorithm to improve the accuracy of
clustering. Taking lubricating oil spectral data as an example, a lubricating oil abrasive particle clustering test was conducted. The test
results show that through two improved particle swarm optimization support vector machine model simulations, the cluster identification of
abrasive particles in aeroengine lubricating oil can be achieved, with an identification accuracy as high as 99.7%, and the monitoring and
diagnosis results are consistent with actual faults. The results provide a basis for oil condition monitoring and fault diagnosis of aeroengine. |
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