Complex chemical process fault diagnosis method based on deep learning multi-model fusion

Abstract

The invention discloses a complex chemical process fault diagnosis method based on deep learning multi-model fusion. According to the invention, two neural networks automatically extract fault features from two aspects respectively, then fuse the features and input the features into a multi-layer perceptron (MLP) for further feature compression and extraction, and finally output a diagnosis result. Features are extracted through a convolutional neural network (CNN) and a long-term and short-term memory (LSTM) respectively, and the features finally extracted by the network have space and time characteristics at the same time; and the final diagnosis is carried out by integrating the characteristics of the two aspects, so that the problem of large calculated amount of the existing traditional diagnosis technology is solved, and the technical problem that the fault diagnosis cannot be accurately carried out in the complex chemical process due to the fact that the characteristics extractedby a single network are too one-sided is also solved.

Description

technical field [0001] The invention relates to a method for diagnosing a fault in a chemical process, in particular to a method for diagnosing a fault in a complex chemical process based on deep learning multi-model fusion. Background technique [0002] Chemical industry is the basic industry for national development. With the development of modern industrial technology and process control technology, chemical technology is developing in the direction of complexity. The process signal data has high dimension, time-varying, non-Gaussian distribution, nonlinear and Strong coupling and other characteristics, and the amount of data is extremely large, and the fault characteristics are difficult to select, which leads to the low accuracy of fault diagnosis in the chemical process, and once a fault occurs, it will cause a series of problems and cause immeasurable losses. Therefore, Accurate and efficient fault diagnosis is of great significance to the safe production of chemical ...

AI Technical Summary

Problems solved by technology

[0002] Chemical industry is the basic industry for national development. With the development of modern industrial technology and process control technology, chemical technology is developing in the direction of complexity. The process signal data has high dimension, time-varying, non-Gaussian distribution, nonlinear and Strong coupling and other characteristics, and the amount of data is extremely large, and the fault characteristics are difficult to select, which leads to the low accuracy of fault diagnosis in the chemical process, and once a fault occurs, it will cause a series of problems and cause immeasurable losses. Therefore, Accurate and efficient fault diagnosis is of great significance to the safe production of chemical process

[0003] The existing traditional fault identification method has a large amount of calculation and requires a certain amount of industrial knowledge. It is difficult to meet the requirements of the existing complex chemical process fault diagnosis in terms of diagnostic accuracy and diagnostic time.

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