基于邻域降噪正交自编码器的工业过程故障检测

针对采用自编码器提取过程特征进行故障检测时，没有考虑数据的局部结构信息，提出邻域降噪正交自编码器（neighborhood denoising quadrature autoencoder, NDQAE)的方法。邻域保持嵌入算法提取数据的邻域信息作为权重对过程数据进行加权，强化数据局部结构信息。正交自编码器进一步提取带有局部信息加权的过程数据非线性特征。通过加入噪声增强自编码器的鲁棒性，并采用反向传播算法训练网络参数，获得能够捕捉数据局部特性和全局特性的鲁棒自编码器模型。在该模型的隐特征和重构残差空间分别构建T2和SPE统计量，并计算统计量控制限用于故障检测。在田纳西-伊斯曼（TE）化工过程和三相流过程进行仿真实验，结果表明了所提算法的有效性。

Neighborhood denoising quadrature autoencoder based fault detection for industrial process

Aiming at using autoencoder to extract process features for fault detection, the local structure information of data is not considered, a method of neighborhood denoising quadrature autoencoder is proposed. The neighborhood preservation embedding algorithm extracts the neighborhood information of the data as a weight to weight the process data and strengthen the local structure information of the data. The orthogonal autoencoder further extracts the nonlinear features of the process data with local information weighting. The robustness of the autoencoder is enhanced by adding noise, and the network parameters are trained by the back-propagation algorithm to obtain a robust autoencoder model that can capture the local and global characteristics of the data. T2 and SPE statistics are constructed in the latent feature and reconstructed residual space of the model, respectively, and the statistical control limits are calculated for fault detection. Simulation experiments are carried out on the Tennessee-Eastman process and the three-phase flow process, and the results show the effectiveness of the proposed algorithm.