Chemical storage tank abnormity detection algorithm research based on FCM-LSTM

Abstract

The invention discloses a chemical storage tank abnormity detection algorithm research based on FCM-LSTM, relates to the chemical equipment and neural network fields. According to the method, a multi-layer network architecture model is used as a fault diagnosis method, the advantages of supervised learning and unsupervised learning are combined, and a fault diagnosis mechanism based on data driving is adopted. The method comprises the following steps that firstly, mass data is clustered by using an unsupervised tool class FCM algorithm; according to a specified similarity standard, datasets are divided, the normal data and the fault data belong to different class clusters; a PSO algorithm is used for avoiding random selection of initial values and accelerating the clustering process, a small amount of mark data is obtained to improve the detection performance, then an LSTM is used for carrying out training network on each cluster and offline historical data, finally, multi-subnet parallel learning is carried out, then results are subjected to fitting combination and integrated analysis, and the generalization capacity of the network is improved. The data size processed by the method is larger, more information can be processed, and the application range is wider.

Description

technical field [0001] The invention relates to the fields of deep learning and industrial control, in particular to the research on the abnormal detection algorithm of chemical storage tanks based on FCM-LSTM. Background technique [0002] With the development of computer technology and Internet technology, the level of industrialization technology is also constantly improving, and industrial development has entered a new normal. With the new development of the Internet of Things, cloud computing, and the continuous maturity of big data technology, we have entered the era of explosive data growth, that is, big data, and the development model of traditional manufacturing is undergoing changes. The integration of modern computer and communication technology and the traditional chemical industry can transform the previous low-efficiency, high-pollution, and high-energy-consumption production methods into a modern sustainable development model with good economic benefits, less ...

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Problems solved by technology

[0003] In the era when the world is gradually moving towards Industry 4.0, due to the relatively late start and development of industrial automation in my country, the safety production infrastructure of some traditional industries is still relatively weak, so the fault detection of industrial equipment is of great significance for improving the reliability of equipment. In industrial application scenarios, we will face such a problem that the relationship between devices is complex and changeable, and it is difficult to use a unified model to represent the status of devices

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