Traditional monitoring methods are trained with normal data and map the process variables into latent variables directly. However, for these methods, the process variables would become intertwined in the latent variables, which results in that the fluctuations of process variables would be submerged in noise or neutralized in latent variables space. In order to address the submergence and neutralization problems, a novel algorithm load weighted denoising autoencoder (LWDAE) is proposed. According to the direction and magnitude of online data, the loading matrix of LWDAE is weighted to highlight the useful information of both training data and online data in latent variables space. In addition, to reduce the effect of noise on weighting matrix, LWDAE modifies the loss function by adding two new regularizations and revises the calculation logic of weighting matrix to consider the successive samples. Case studies of continuous stirred tank reactor demonstrate the effectiveness of LWDAE.

传统的监视方法是使用正常数据训练的，并将过程变量直接映射到潜在变量。然而，对于这些方法，过程变量将与潜在变量交织在一起，这导致过程变量的波动将被淹没在噪声中或在潜在变量空间中被抵消。为了解决淹没和中和问题，提出了一种新的算法负载加权降噪自编码器（LWDAE）。根据在线数据的方向和大小，对LWDAE的加载矩阵进行加权，以突出潜在变量空间中训练数据和在线数据的有用信息。另外，为减少噪声对加权矩阵的影响，LWDAE通过添加两个新的正则化修改损失函数，并修改加权矩阵的计算逻辑以考虑连续样本。连续搅拌釜反应器的案例研究证明了LWDAE的有效性。