A hierarchical deep learning framework for combined rolling bearing fault localization and identification with data fusion
A hierarchical deep learning framework for combined rolling bearing fault localization and identification with data fusion
复制标题
滚动轴承故障定位与识别与数据融合相结合的分层深度学习框架
DOI：
10.1177/10775463221091601
复制
发表时间：
2022-04
影响因子：
2.8
通讯作者：
Mingxuan Liang;Kai Zhou
中科院分区：
文献类型：
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作者：
Mingxuan Liang;Kai Zhou
滚动轴承故障诊断是一个重要的研究课题，基于数据驱动的深度学习技术得到了广泛的应用。虽然最先进的研究表明，在轴承故障诊断的实质性进展，他们大多是在假设，轴承容易发生故障的位置已经知道。然而，在实际应用中，许多滚动轴承安装在复杂的机械系统中，其中任何一个都可能发生故障。因此，故障诊断本质上是一个过程，以实现故障定位和识别，这导致许多故障场景要处理。这将显著降低使用传统深度学习分析的故障诊断性能。在这项研究中，我们的目标是开发一个新的深度学习框架来应对上述挑战。我们特别设计了一个分层深度学习框架，该框架由基于迁移学习的多个顺序部署的深度学习模型组成。这可以提高学习充分性的高维问题，涉及许多故障情况下，即使在有限的数据集，从而提高故障诊断性能。在没有关于故障定位的先验知识的情况下，这种方法非常有利于传感器/数据融合，其充分利用从不同加速度计获取的测量中的丰富的枢轴故障相关特征。系统的案例研究，使用公开访问的实验滚动轴承数据集进行验证这种新的方法。
Fault diagnosis of rolling bearings becomes an important research subject, where the data-driven deep learning-based techniques have been extensively exploited. While the state-of-the-art research has shown the substantial progresses in bearing fault diagnosis, they mostly were implemented upon the hypothesis that the location of bearing prone to failure already is known. Nevertheless, in actual practice many rolling bearings are installed in a complex machinery system, any of which is likely subject to fault. As such, fault diagnosis essentially is a process to achieve both fault localization and identification, which results in many fault scenarios to be handled. This will significantly degrade the fault diagnosis performance using conventional deep learning analysis. In this research, we aim to develop a new deep learning framework to address abovementioned challenge. We particularly design a hierarchical deep learning framework consisting of multiple sequentially deployed deep learning models built upon the transfer learning. This can improve the learning adequacy for a high-dimensional problem with many fault scenarios involved even under limited dataset, thereby enhancing the fault diagnosis performance. Without the prior knowledge regarding the fault location, this methodology is greatly favored by the sensor/data fusion which takes full advantage of the enriched pivot fault-related features in the measurements acquired from different accelerometers. Systematic case studies using the publicly accessible experimental rolling bearing dataset are carried out to validate this new methodology.