关键词: 小样本, 不均衡数据, AdaBoost算法, 合成少数类过采样技术, 剩余寿命预测

Abstract: In view of the poor processing effect of AdaBoost for small sample unbalanced equipment health monitoring data,an equipment health state analysis and life prediction model were proposed based on clipping oversampling add AdaBoost algorithm.The sample weight distribution and capacity were calculated based on AdaBoost,the improved clipping coefficient was generated according to the maximum weight and the number of samples,and the samples with weight greater than the clipping coefficient were selectively processed,so as to improve the calculation efficiency.The error classification sample weights were filtered by the class k nearest neighbor rule,and then the synthetic minority oversampling technology was introduced to improve the number of sample weights,so as to effectively avoid the over fitting problem caused by unbalanced data sets in the iterative process.The equipment life was predicted by accurately classifying the equipment operation state and fitting the time-related equipment life curve.The example results showed that the proposed model could effectively analyze the equipment health status under unbalanced data,and could also effectively predict the remaining life.

Key words: small sample, unbalanced data, Adaboost, synthetic minority oversampling technology, remaining life prediction