论文标题
对比性盲目denoing AutoCododer用于实时降级工业物联网传感器数据
Contrastive Blind Denoising Autoencoder for Real-Time Denoising of Industrial IoT Sensor Data
论文作者
论文摘要
在工业物联网环境中，当数据驱动算法在控制系统的上层上运行时，必须确保传感器数据的质量。不幸的是，工业设施中的共同位置是找到传感器时间序列，噪音和异常值严重腐败。在这项工作中，提出了一种基于盲人的自动编码器基于数据驱动的基于数据驱动的学习方法，以实时降级工业传感器数据。 \ textit {blind}一词强调，与典型的DeNoing自动编码器相反，在需要关于噪声的先验知识的情况下，不需要关于噪声的先验知识。通过在自动编码器的潜在空间上使用噪声对比估计（NCE）正则化来实现盲目降级，这不仅有助于DeNoise，还可以引起有意义且平稳的潜在空间。在模拟系统和实际工业过程中的实验评估表明，所提出的技术的表现优于经典的denoising方法。
In an industrial IoT setting, ensuring the quality of sensor data is a must when data-driven algorithms operate on the upper layers of the control system. Unfortunately, the common place in industrial facilities is to find sensor time series heavily corrupted by noise and outliers. In this work, a purely data-driven self-supervised learning-based approach based on a blind denoising autoencoder is proposed for real time denoising of industrial sensor data. The term \textit{blind} stresses that no prior knowledge about the noise is required for denoising, in contrast to typical denoising autoencoders where prior knowledge about the noise is required. Blind denoising is achieved by using a noise contrastive estimation (NCE) regularization on the latent space of the autoencoder, which not only helps to denoise but also induces a meaningful and smooth latent space. Experimental evaluation in both a simulated system and a real industrial process shows that the proposed technique outperforms classical denoising methods.
