A fault intelligent prediction and diagnosis method for chemical process

Abstract

This invention provides an intelligent fault prediction and diagnosis method for chemical processes, comprising the following steps: S1. Data acquisition: Collecting monitorable data from chemical processes using chemical sensors; S2. Data preprocessing: Decomposing the collected data into spatial scales using variational mode decomposition to obtain the positional dependencies of different sensors, and iteratively optimizing the decomposition parameters using a beaver optimizer; S3. Constructing a deep learning model for time-frequency domain feature fusion: Based on a common convolutional block attention module, an improved feature-injected convolutional block attention module is proposed; based on a temporal convolutional neural network, an improved frequency-domain convolutional module is proposed to further extract features; S4. Deep learning model training: Using a cross-entropy loss function and an Adam optimizer, the preprocessed data is divided into training, validation, and test sets and input into the model for multiple training iterations; S5. Evaluation of diagnostic results: The diagnostic performance of the model is comprehensively evaluated using recall, F1 score, and area under the receiver operating characteristic curve, and the best-performing model is selected from multiple trained models for deployment. This method enables real-time extraction and fusion of time-frequency domain features from chemical sensors, achieving efficient fault prediction and diagnosis in chemical processes.

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