Process fault analysis device of process industry system and method therefor

Abstract

The invention relates to a process industry system fault analysis device based on complex network theories and a method therefor. The device comprises a system structure information base, a man-machine interaction module, a process data management module, a system network characteristics analysis module and a process fault analysis module. The process fault analysis device and the method providedby the invention can be employed for identifying the key parts of the process system, reducing the process monitoring variables and solving the problem that the selection of monitoring point positiondepends on people's knowledge or experience; meanwhile, the invention makes the best of real-time data information of the process industry process system, thereby ensuring monitoring over the processfault is more instantaneous and accurate; and the utilization of the system domain knowledge improves the fault recognition and separation capability of the traditional PCA monitoring method.

Description

technical field [0001] The invention relates to process industry systems based on complex network theory, in particular to a process failure analysis device and method for process industry systems. Background technique [0002] In the process industry, due to the continuous expansion of the scale and complexity of the industrial process, the requirements for the safety and reliability of the production system are also increasing. The safe, reliable, and stable operation of the production process has become a modern industry. important task. For this reason, during the operation of the system, it is necessary to detect the occurrence of faults or abnormalities in time, and to judge the type of fault and locate the source of the fault to eliminate adverse factors. Traditional process anomaly monitoring methods can be divided into three categories: analytical-based, knowledge-based and data-driven methods. Analytical-based methods are based on strict mathematical models, such...

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Problems solved by technology

[0003] However, in practical applications, the primary problem of data-driven methods is the selection of data monitoring points, that is, which points are necessary and important for process monitoring among the hundreds or thousands of monitoring points in the system Yes, in fact, technicians usually only pay attention to some important monitoring points in empirical knowledge; moreover, because the data-driven method does not utilize the domain knowledge of the process system, its ability to identify and separate abnormalities will be limited (although it is known A certain variable produces anomalies, but it is difficult to determine the root cause); in addition, for large and complex systems, if all process variables are monitored, more noise information will undoubtedly be generated, which will increase the computational burden of fault monitoring and analysis

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