This paper focuses on the issue of anomaly detection for data in process control systems (PCSs). Considering data features in PCSs, this paper proposes to utilize the notion of one-class classification (OCC). In order to provide a general solution for more types of systems, ensemble learning is combined with OCC models. Two different ensembles of OCC models are proposed based on different scenarios in the process of detection. Performance of the proposed detection scheme is validated via several UCI datasets and two practical PCSs.

Excessive nitrogen oxide (NOx) emissions result in growing environmental problems and increasingly stringent emission standards. This requires a precise control for NOx emissions. A prerequisite for precise control is accurate NOx emission detection. However, the NOx measurement sensors currently in use have serious lag problems in measurement due to the harsh operating environment and other problems. To address this issue, we need to make long-term prediction for NOx emissions. In this paper, we propose a long-term prediction model based on LSTM–Transformer. First, the model uses self-attention to capture long-term trend. Second, long short-term memory network (LSTM) is used to capture short-term trends and as secondary position encoding to provide positional information. We construct them using a parallel structure. In long-term prediction, experimental results on two real datasets with different sampling intervals show that the proposed prediction model performs better than the currently popular methods, with 28.2% and 19.1% relative average improvements on the two datasets, respectively.