Diagnosis method of chemical process operation failure

Abstract

The invention relates to the field of construction machinery detection, and discloses a method for diagnosing operating failures of chemical processes. The diagnosis method includes: identifying abnormal parameters in the operation data in the chemical process; identifying the corresponding fault root based on the abnormal parameters in the operation data; and based on the preset characteristic parameters in the operation data, Quantitative health diagnosis based on the process operation state in the chemical process. The present invention can provide a systematic solution for deep fault identification and process health diagnosis of the device.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a method for diagnosing chemical process operation failures. Background technique [0002] The petrochemical production process is mostly high temperature and high pressure, flammable and explosive, and the medium is toxic and harmful. Once an accident occurs, it will cause great harm to the natural environment and social public safety. Therefore, strengthening the safe and efficient management of the petrochemical process is a necessary condition for the sustainable development of the petrochemical industry. [0003] At present, my country's petrochemical industry lacks integrated software and systems for abnormal working condition diagnosis and safe optimized operation, and there is still a big gap between intelligent risk management and control. The key problems that make the abnormal diagnosis of petrochemical process and safety optimization operation mainly include:...

AI Technical Summary

Problems solved by technology

The key issues that cause difficulties in abnormal diagnosis and safe optimization of petrochemical processes mainly include: (1) The production process has multiple sources of data, and the abnormal conditions that occur are characterized by strong concealment and many factors involved. It is difficult for plant operators to quickly and Accurate detection of abnormal signs may easily lead to misjudgment or even misoperation; (2) The timing of variable changes in the petrochemical production process has a strong correlation, and its fault is often not a simple alarm signal, but a combination of timing patterns of a group of variable signals. The large number and disorder make the real-time monitoring and fault diagnosis of large-scale and complex systems unsatisfactory; (3) The real-time monitoring units on the industrial site are often in an independent working state, lacking an effective information fusion mechanism, and the ability to display global information is poor. Meet the needs of abnormal working condition identification and root cause analysis in complex environments

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