A novel quality‐related process monitoring method for multi‐unit industrial processes under incomplete data conditions

Abstract Modern industrial processes usually consist of multiple local units. With the wide application of distributed control systems, a large amount of industrial data is collected. During data acquisition and transmission, outliers and missing data commonly exist in different local units. Under such incomplete data conditions, the detection performance of traditional quality‐related process monitoring methods may be seriously degraded. This study develops a novel quality‐related process monitoring method for multi‐unit industrial processes. Considering the existence of outliers and missing data, incomplete data preprocessing is first performed on raw process data and quality data. Then, process variables are divided into several blocks according to the process knowledge. Afterwards, the quality‐related features extracted by variational information bottleneck (VIB) are used to train deep support vector data description (DSVDD). Finally, a quality‐related process monitoring strategy is designed at both block and global levels. Experimental results on the revised Tennessee Eastman process demonstrate that the proposed method has better monitoring performance under incomplete data conditions than other state‐of‐the‐art methods.