收稿日期: 2021-05-18
网络出版日期: 2022-07-11
基金资助
国家重点研发计划项目(2020YFB1711100);国家自然科学基金(52005327);国家自然科学基金(72001138);国家自然科学基金(72071127)
Intelligent Bearing Fault Diagnosis Based on Adaptive Deep Belief Network Under Variable Working Conditions
Received date: 2021-05-18
Online published: 2022-07-11
机械设备服役过程中,工作环境和运转状态的动态变化直接影响设备故障诊断正确率,导致时间成本和经济效益的损失.优化深度置信网络结构,结合固定学习步长的信号分解技术,保留传感器数据原始特征,逐层反复提取信号的深层关键信息,并集成数据丢失技术优化网络结构,可以规避过拟合问题.进一步,结合迁移学习中的领域自适应方法,固化不同层级深度置信网络的记忆特征,形成考虑平移不变特征的自适应深度置信网络,识别变工况下同类故障信号特征信息,提升轴承智能故障诊断的准确性和泛化性.基于滚动轴承公开数据集,不同工况下该方法平均正确率高达95.65%,与其他5种方法相比较,证实了本文方法的有效性与准确性.
马航宇, 周笛, 卫宇杰, 吴伟, 潘尔顺 . 变工况下基于自适应深度置信网络的轴承智能故障诊断[J]. 上海交通大学学报, 2022 , 56(10) : 1368 -1377 . DOI: 10.16183/j.cnki.jsjtu.2021.161
In engineering, working environment and operating state are constantly changing, which decreases the correct rate of equipment fault diagnosis, resulting in the loss of time and cost. The structure of the deep belief network is investigated for the time-varying factors in the mechanical system. In combination with the signal decomposition technology of fixed learning step size, the original characteristics of the sensor data are retained. In addition, the deep key information of the signal is repeatedly extracted layer by layer. The data loss technology is integrated to optimize the network structure to avoid over-fitting problems. Further, considering the domain adaptive method in transfer learning, the memory characteristics of different levels of deep belief networks are solidified. Therefore, a domain adaptive deep belief network with shift-invariant features (SIF-DADBN) is proposed for rolling bearing fault diagnosis. By identifying the characteristic information of similar fault signals with variable working conditions, the accuracy and generalization of bearing intelligent fault diagnosis are both improved. Based on the public data set of rolling bearings, the average correct rate of the fault diagnosis technology is found to be as high as 95.65%. Compared with five other methods, the effectiveness and accuracy of SIF-DADBN under variable working conditions are verified.
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