Systems Engineering and Electronics ›› 2018, Vol. 40 ›› Issue (1): 114-118.doi: 10.3969/j.issn.1001-506X.2018.01.17

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Airborne equipment fault probability prediction based on interpolation-fitting-transfer learning algorithm

GU Taoyong, GUO Jiansheng, LI Zhengxin, WANG Jian, WANG Tengjiao   

  1. Equipment Management and Security Engineering College, Air Force Engineering University, Xi’an 710051, China
  • Online:2018-01-08 Published:2018-01-08

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Abstract:

To solve the multi-operating environment airborne equipment fault probability prediction problem, a self-adaptive weighted interpolation-fitting-transfer learning (ITF) algorithm is proposed. On the basis of data size and data feature (distribution similarity and information entropy), the algorithm adjusts the weight of interpolation, fitting and transfer learning. The conventional interpolation and fitting method can smooth the fault frequency curve, and transfer learning can reduce the prediction risk caused by data dilution. The analysis and simulation demonstrate that the ITF algorithm is acceptable in time complexity and has higher prediction accuracy.

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