| 冯兆熙,邱度金,孔令驹,孙 成,邓耀华.基于深度置信网络的轴承剩余使用寿命预测[J].电子测量与仪器学报,2021,35(10):124-129 |
| 基于深度置信网络的轴承剩余使用寿命预测 |
| Remaining useful life prediction of bearing based on deep belief network |
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| DOI： |
| 中文关键词: 剩余使用寿命预测 深度置信网络 特征自提取 能量概率模型 |
| 英文关键词:remaining useful life prediction deep belief network feature self-extraction energy probability model |
| 基金项目:广东省重点领域研发计划项目（2019B010154001）资助 |
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| 中文摘要: |
| 针对精密电子、塑形成型等高速高精加工过程滚动轴承的剩余使用寿命预测建模中存在样本少、标注难度大等问题,引
入深度置信网络,融合无监督与有监督微调学习方法开展滚动轴承剩余使用寿命预测研究。 将滚动轴承的振动数据特征作为
输入、剩余使用寿命作为输出,以能量函数量化特征准确性的概率分布作为基本组成部件,部件的上一层特征输出作为下一层
的输入,将多个这样的部件首尾相接,构建滚动轴承剩余使用寿命预测模型。 通过原始数据的无监督预训练得到模型中各个单
元的初始参数,然后利用剩余使用寿命标签数据进行模型的有监督微调,进一步提高模型预测的准确性。 实验结果表明,所提
出的方法能够对滚动轴承的剩余使用寿命进行预测,与支持向量回归( SVR)和主成分分析-深度置信网络(PCA-DBN)方法进
行比较,预测误差分别减少 28. 48%、5. 57%,该方法在现场预测方面,具有更高的预测准确度,而且本方法还能减少对专家知识
的依赖,模型的泛化能力更强。 |
| 英文摘要: |
| Aiming at the problems of few samples and difficult labeling in the remaining useful life prediction modeling of rolling bearings
in high-speed and high-precision machining processes such as precision electronics and plastic shaping, this paper introduces the deep
belief network that integrates the unsupervised and supervised fine-tuning learning methods to carry out the research on the prediction of
the residual service life of rolling bearings. The vibration data features of rolling bearing are taken as input and the remaining useful life
as output. The probability distribution of features accuracy quantified by energy function is taken as the basic component, and the feature
output of the previous layer of the components is taken as the input of the next layer. The remaining useful life prediction model of rolling
bearing is constructed by connecting multiple such components head to tail. The initial parameters of each unit in the model are obtained
by unsupervised pre training of the original data, then the supervised fine-tuning of the model is carried out by using the remaining useful
life label data to further improve the accuracy of the model prediction. The experimental results show that the method proposed in this
paper can predict the remaining service life of rolling bearing. Compared with SVR and PCA-DBN, the prediction error is reduced by
28. 48% and 5. 57% respectively. This method has higher prediction accuracy in field prediction, and it can reduce the dependence on
expert knowledge as well as improve generalization ability. |
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