Process Monitoring Based on Orthogonal Locality Preserving Projection with Maximum Likelihood Estimation

One-dimensional convolutional neural network-based active feature extraction for fault detection and diagnosis of industrial processes and its understanding via visualization

Feature extraction from process signals enables process monitoring models to be effective in industrial processes. Deep learning presents extensive possibilities for extracting abstract features from image and visual data. However, the main inputs of conventional deep neural networks are large images. To overcome this, a one-dimension convolution neural network-based model optimized by a reinforcement-learning-based neural architecture search, is proposed for multivariate processes control. The experimental results illustrate its predominance for detecting and recognizing process faults. Feature and network visualization are also implemented to explore the reasons for its outstanding performance. This research extends the applications of convolutional neural network based on one-dimension process signals in complex multivariate process control.

Chemical static equipment commonly used sensor fault detection and isolation methods

This paper conducts a study on the faults of common sensors involved in chemical static equipment. Firstly, the types and characteristics of commonly used sensors of chemical static equipment are analyzed, and the characteristics of sensor output signal changes are summarized with the working characteristics of chemical equipment. Then the faults of static equipment sensors are classified and a fault model is established. Through the study of sensor fault detection and isolation methods at home and abroad, the overall scheme of sensor system fault detection and isolation combining single sensor fault detection and isolation method and multi-sensor fault detection and isolation method is proposed. According to the characteristics that chemical processes are generally in a dynamic and stable state and there is a certain correlation between the signals of each detection point in the equipment, a sensor system model is established by using the correlation of multiple sensors on the equipment, and when a sensor in the sensor system fails, the system model changes beyond the threshold value, and a different form of residual generation is used to determine which sensor is faulty and achieve the detection and isolation of faulty sensors. The fault detection method is simulated and studied by using relevant software, combined with a support vector machine and neural network toolbox. The results show that the method proposed in this paper can effectively complete the fault detection and isolation of sensors commonly used in chemical static equipment. The accuracy and reliability of the prediction model are high.