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首页> 外文期刊>IEEE transactions on industrial informatics >Diagnosing Rotating Machines With Weakly Supervised Data Using Deep Transfer Learning
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Diagnosing Rotating Machines With Weakly Supervised Data Using Deep Transfer Learning

机译：使用深度转移学习诊断旋转机器与弱监督数据

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摘要

Rotating machinery fault diagnosis problems have been well-addressed when sufficient supervised data of the tested machine are available using the latest data-driven methods. However, it is still challenging to develop effective diagnostic method with insufficient training data, which is highly demanded in real-industrial scenarios, since high-quality data are usually difficult and expensive to collect. Considering the underlying similarities of rotating machines, data mining on different but related equipments potentially benefit the diagnostic performance on the target machine. Therefore, a novel transfer learning method for diagnostics based on deep learning is proposed in this article, where the diagnostic knowledge learned from sufficient supervised data of multiple rotating machines is transferred to the target equipment with domain adversarial training. Different from the existing studies, a more generalized transfer learning problem with different label spaces of domains is investigated, and different fault severities are also considered in fault diagnostics. The experimental results on four datasets validate the effectiveness of the proposed method, and show it is feasible and promising to explore different datasets to improve diagnostic performance.
机译：当使用最新的数据驱动方法可用时，旋转机械故障诊断问题已经很好地解决了测试机器的充分监督数据。然而，开发有效的诊断方法仍然具有挑战性，培训数据不足，这在实际工业场景中非常苛刻，因为高质量的数据通常难以收集。考虑到旋转机器的潜在相似之处，不同但相关设备上的数据挖掘可能会使目标机器上的诊断性能受益。因此，本文提出了一种基于深度学习的基于深度学习的诊断的新型转移学习方法，其中从多旋转机器的充分监督数据中学习的诊断知识被转移到目标设备，具有域对抗性培训。不同于现有研究，研究了具有不同标签空间的更广泛的转移学习问题，并且在故障诊断中也考虑了不同的故障严重程度。四个数据集的实验结果验证了所提出的方法的有效性，并表明探索不同的数据集是可行和有前途的，以改善诊断性能。

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