Multivariate statistical process control (MSPC) has been widely employed for process fault
detection. Recently, deep neural networks (DNNs), ie, stacked autoencoder (SAE) enjoys its …

The massive high-dimensional measurements accumulated by distributed control systems
bring great computational and modeling complexity to the traditional fault diagnosis …

海量高维度的过程测量信息给传统的故障诊断算法带来极大的计算复杂度和建模复杂度,
且传统诊断算法存在难以利用高阶量进行在线估计的不足. 鉴于深度学**技术**大的数据表示 …

Deep neural networks (DNNs) are popular in process monitoring for its remarkable feature
extraction from data. However, the increased dimension and correlation of the process …

Data-based process monitoring methods have received tremendous attention in recent
years, and modern industrial process data often exhibit dynamic and nonlinear …

Learning an efficient feature representation from wind power data is crucial for improving the
accuracy of wind power forecasting (WPF). Compared to supervised learning-based models …