Aeroengine fault data tag using on improved BDPCA algorithm
LV Chao1, CHENG Gong2,LIU Yunqing1
1.National Demonstration Center for Experimental Electrical and Electronic Technology, Chanchun
University of Science and Techonlgy, ChangChun 130022, China;
2.No.63850 Unit of PLA, Baicheng 137000, China
Abstract：As the power core of an aircraft,the aeroengine plays an important role in the safe flight of the aircraft.It is of great significance to ensure the smooth operation of aeroengine for flight safety.While the technology of aeroengine fault diagnosis based on supervised learning has been progressing continuously, how to convert a large number of unlabeled data obtained at ordinary time into labeled data that can be used to train the fault diagnosis model has become a bottleneck restricting the development of the industry.In the paper, the DPCA algorithm based on unsupervised learning was introduced to realize the accurate classification and marking of unmarked data sets.Aiming at some defects of the DPCA algorithm, the algorithm was optimized by using the shared neighborhood algorithm and BIC selection criteria.Finally, the performance of the improved algorithm was verified by applying gas path fault data of some aeroengine, and good results were obtained.
吕超1,程弓2,刘云清1. 基于BDPCA聚类算法的航空发动机故障数据标记[J]. 振动与冲击, 2020, 39(9): 35-41.
LV Chao1, CHENG Gong2,LIU Yunqing1. Aeroengine fault data tag using on improved BDPCA algorithm. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(9): 35-41.
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