Chemical process operation fault diagnosis system

Abstract

The invention relates to the field of engineering machinery detection, and discloses a chemical process operation fault diagnosis system. The diagnosis system comprises an abnormal parameter identification device used for identifying abnormal parameters in operation data in the chemical process, a fault source identification device used for identifying a corresponding fault source based on the abnormal parameters in the operation data, and a health diagnosis device used for carrying out quantitative health diagnosis on the process operation state in the chemical process on the basis of presetcharacteristic parameters in the operation data. A systematic solution can be provided 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 diagnostic system for operating faults in chemical processes. Background technique [0002] Most petrochemical production processes are high temperature and high pressure, flammable and explosive, and toxic and harmful media. 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 safety optimization operation, and there is still a big gap from intelligent risk management and control. The key issues that cause difficulties in abnormal diagnosis and safe optimization of petrochemical processes mainly include: (1) ...

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

Images