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首页> 外文期刊>IEEE transactions on industrial informatics >FEM Simulation-Based Generative Adversarial Networks to Detect Bearing Faults
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FEM Simulation-Based Generative Adversarial Networks to Detect Bearing Faults

机译：基于FEM模拟的生成对抗网络来检测轴承故障

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摘要

Complete fault sample is essential to activate artificial intelligent (AI) models. A novel fault detection scheme is proposed to build a bridge between AI and real-world running mechanical systems. First, the finite element method simulation is used to simulate samples with different faults to overcome the shortcoming of missing fault samples. Second, to enlarge datasets, new samples similar to the simulation and measurement fault samples are generated by generative adversarial networks and further combined with the original simulation and measurement samples to obtain synthetic samples. Finally, the synthetic and unknown fault samples are severed as the training and test samples, respectively, to the classifiers of AI models, and the unknown fault types will be finally determined. A public datasets of bearings have been used to verify the effectiveness of the proposed scheme. It is expected that the proposed scheme can be extended to complex mechanical systems.
机译：完整的故障样本对于激活人工智能（AI）模型至关重要。提出了一种新的故障检测方案，在AI和现实世界运行的机械系统之间构建桥梁。首先，有限元方法模拟用于模拟具有不同故障的样本，以克服缺失故障样本的缺点。其次，为了放大数据集，通过生成的对抗网络产生类似于模拟和测量故障样本的新样本，并进一步与原始模拟和测量样品相结合以获得合成样品。最后，将合成和未知的故障样本分别被切断为培训和测试样本，分别为AI模型的分类器，最终将确定未知的故障类型。轴承的公共数据集已用于验证拟议方案的有效性。预计该方案可以扩展到复杂的机械系统。

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