| 佐 磊,胡小敏,何怡刚,孙洪凯,李 兵.小样本数据处理的加速寿命预测方法[J].电子测量与仪器学报,2020,34(11):26-32 |
| 小样本数据处理的加速寿命预测方法 |
| Accelerated life prediction method for small sample data |
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| DOI： |
| 中文关键词: 小样本失效数据 数据处理 Bayes Bootstrap & k-means 寿命预测 |
| 英文关键词:small sample failure data data processing Bayes Bootstrap & k-means lifetime prediction |
| 基金项目:装备预先研究重点项目(41402040301)、国家重点研发计划(20l6YFF0102200)、国家自然科学基金重点项目(51637004)、国家自然科学基金(51777050,51577046)资助项目 |
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| Author | Institution |
| Zuo Lei | 1. School of Electrical Engineering and Automation, Hefei University of Technology,2. National Local Joint Engineering Laboratory of Renewable Energy Access to Power Grid Technology,
Hefei University of Technology |
| Hu Xiaomin | 1. School of Electrical Engineering and Automation, Hefei University of Technology |
| He Yigang | 1. School of Electrical Engineering and Automation, Hefei University of Technology,2. National Local Joint Engineering Laboratory of Renewable Energy Access to Power Grid Technology,Hefei University of Technology，3. School of Electrical Engineering and Automation, Wuhan University |
| Sun Hongkai | 1. School of Electrical Engineering and Automation, Hefei University of Technology |
| Li Bing | 1. School of Electrical Engineering and Automation, Hefei University of Technology,2. National Local Joint Engineering Laboratory of Renewable Energy Access to Power Grid Technology,Hefei University of Technology |
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| 中文摘要: |
| 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。 |
| 英文摘要: |
| The Bayes Bootstrap method is widely used in the field of small sample prediction. However, due to the random value points
that are not conducive to the prediction accuracy in the randomly generated self-service sample, the prediction deviation is large. In view
of this deficiency, this paper proposes the Bayes Bootstrap & k-means method. In the case of having small sample failure data, use the
Bayes Bootstrap method to generate self-service samples to expand the capacity of the original life data firstly, and then use the k-means
method to perform data clustering analysis to remove outliers as much as possible and filter out more data points that meet the forecasting
rules. Finally, the multi-chip module interconnection structure double stress accelerated life prediction is used as an example to verify
the calculation method. Compared with the Bayes Bootstrap method, the prediction accuracy is improved by about 81. 44%, which has
certain engineering significance. |
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