小样本数据处理的加速寿命预测方法

Bayes Bootstrap法在小样本预测领域应用成熟广泛,但由于其随机产生的自助样本中存在不利于预测精度的野值点造成预测偏差较大,针对此不足,提出Bayes Bootstrap&k-means方法。在拥有小样本失效数据情况下,首先采用Bayes Bootstrap法产生自助样本对原有寿命数据进行容量扩充,再采用k-means方法对其进行数据聚类分析,尽可能去除野值点,筛选出更加符合预测规律的数据点。最后以多芯片组件互连结构双应力加速寿命进行预测为例计算验证了该方法相比仅采用Bayes Bootstrap法,预测精度提高了约81.44%,有一定的工程意义。

Accelerated life prediction method for small sample data

The Bayes Bootstrap method is widely used in the field of small sample prediction. However, due to the random value pointsthat are not conducive to the prediction accuracy in the randomly generated self-service sample, the prediction deviation is large. In viewof this deficiency, this paper proposes the Bayes Bootstrap & k-means method. In the case of having small sample failure data, use theBayes Bootstrap method to generate self-service samples to expand the capacity of the original life data firstly, and then use the k-meansmethod to perform data clustering analysis to remove outliers as much as possible and filter out more data points that meet the forecastingrules. Finally, the multi-chip module interconnection structure double stress accelerated life prediction is used as an example to verifythe calculation method. Compared with the Bayes Bootstrap method, the prediction accuracy is improved by about 81. 44%, which hascertain engineering significance.