Condition monitoring (CM) of machinery is important for ensuring the reliability of industrial processes. To adapt to the rareness of data from faulted machinery, semisupervised CM can be implemented by training on only healthy samples. However, the performance of CM can be impaired by the variability of operating data acquired from complex machinery. Additionally, the accuracy of results is limited by the impractical assumption that samples under different health conditions are naturally separable. To address these problems, an adversarial representation learning method is developed herein. The method is trained by reconstructing operating data in both signal and latent spaces, and adversarial evolution is adopted to avoid the convergence at local optima. In this case, data representations of health conditions can be obtained to suppress the volatility of measurements, and redundant information can be reduced by latent codes. Moreover, a strategy of representation embedding is developed to impose constraints on unhealthy data, guaranteeing separable samples under distinct health conditions in the monitoring stage. Furthermore, feature fusion is conducted to avoid missing detailed information on health conditions. The satisfactory performance of the proposed method is demonstrated by experiments in test benches and actual scenarios of wind power generation.

中文翻译：

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复杂机械智能状态监测的对抗表示学习

机械的状态监测 (CM) 对于确保工业过程的可靠性非常重要。为了适应故障机器数据的稀缺性，半监督 CM 可以通过仅对健康样本进行训练来实现。然而，CM 的性能可能会受到从复杂机器中获取的操作数据的可变性的影响。此外，结果的准确性受到不切实际的假设的限制，即不同健康状况下的样本自然可分离。为了解决这些问题，本文开发了一种对抗性表示学习方法。该方法通过在信号空间和潜在空间中重建操作数据来训练，并采用对抗性进化来避免收敛于局部最优。在这种情况下，可以获得健康状况的数据表示以抑制测量的波动，并且可以通过潜在代码减少冗余信息。此外，还开发了一种表示嵌入策略来对不健康的数据施加约束，保证在监测阶段不同健康状况下的可分离样本。此外，进行特征融合以避免遗漏有关健康状况的详细信息。试验台架实验和风力发电实际场景证明了所提方法的令人满意的性能。保证监测阶段不同健康状况下的样本可分离。此外，进行特征融合以避免遗漏有关健康状况的详细信息。通过试验台架实验和风力发电的实际场景，证明了所提方法的令人满意的性能。保证监测阶段不同健康状况下的样本可分离。此外，进行特征融合以避免遗漏有关健康状况的详细信息。试验台架实验和风力发电实际场景证明了所提方法的令人满意的性能。