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Intelligent Condition-Based Monitoring Techniques for Bearing Fault Diagnosis

机译：基于智能条件的轴承故障诊断监控技术

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摘要

In recent years, intelligent condition-based monitoring of rotary machinery systems has become a major research focus of machine fault diagnosis. In condition-based monitoring, it is challenging to form a large-scale well-annotated dataset due to the expense of data acquisition and costly annotation. The generated data have a large number of redundant features which degraded the performance of the machine learning models. To overcome this, we have utilized the advantages of minimum redundancy maximum relevance (mRMR) and transfer learning with a deep learning model. In this work, mRMR is combined with deep learning and deep transfer learning framework to improve the fault diagnostics performance in terms of accuracy and computational complexity. The mRMR reduces the redundant information from data and increases the deep learning performance, whereas transfer learning, reduces a large amount of data dependency for training the model. In the proposed work, two frameworks, i.e., mRMR with deep learning and mRMR with deep transfer learning, have explored and validated on CWRU and IMS rolling element bearings datasets. The analysis shows that the proposed frameworks can obtain better diagnostic accuracy compared to existing methods and can handle the data with a large number of features more quickly.
机译：近年来，基于智能化的旋转机械系统的监测已成为机器故障诊断的主要研究重点。在基于条件的监测中，由于数据采集和成本昂贵的注释，构建了一个大规模的注释数据集是具有挑战性的。生成的数据具有大量冗余功能，可降低机器学习模型的性能。为了克服这一点，我们利用了最小冗余最大相关性（MRMR）的优势，并通过深入学习模型转移学习。在这项工作中，MRMR与深度学习和深度转移学习框架相结合，以提高准确性和计算复杂性的故障诊断性能。 MRMR从数据中减少了冗余信息并提高了深度学习性能，而转移学习，减少了对培训模型的大量数据依赖性。在拟议的工作中，在CWRU和IMS滚动元件轴承数据集上探索和验证了两个框架，即MRMR。分析表明，与现有方法相比，所提出的框架可以获得更好的诊断准确性，并且可以更快地处理具有大量功能的数据。

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