Method for patterning Petri network and application of method for patterning Petri network in diagnosis of fault of marine gas turbine

Abstract

The invention provides a method for patterning a Petri network. The method comprises the following steps of: 1, reading out fault data from a database by using a fault Petri network generation algorithm, constructing a fault Petri network, automatically generating coordinates and displaying the coordinates on an interface; and 2, reading the data on the fault Petri network displayed in the form of a pattern and storing the data into the database so as to realize storage of fault information and diagnosis information. By the method, the shortcoming that most of the conventional fault diagnosissystems manage and diagnose the faults with a simple word mode and have no self-learning functions is overcome. By using a patterned fault diagnosis technology, a maintainer can find out abnormity inadvance and immediately determine a fault reason. Sudden events can be prevented, the service expiration and the service time of equipment can be prolonged, and the cost is saved. The invention also discloses application of the method for patterning the Petri network to diagnosis of the fault of a marine gas turbine.

Description

technical field [0001] The invention belongs to the technical field of marine gas turbine fault diagnosis, more specifically, it is a Petri net graphical method, and the invention also relates to the application of the Petri net graphical method in the marine gas turbine fault diagnosis. Background technique [0002] The gas turbine equipment is complex, and the components of the main engine and the subsidiary subsystems are tightly coupled together through thermomechanical relations. After a fault occurs, the causal relationship of the fault is very complicated, and it is difficult to judge, analyze, and eliminate the fault; after a fault occurs, it is difficult to accurately analyzed and eliminated in a timely manner. [0003] Traditional diagnostic techniques can no longer meet the needs, and rapid and accurate fault diagnosis has become an inevitable development trend. Fault diagnosis tasks that used to take several days or even longer to complete must now be compressed...

AI Technical Summary

Problems solved by technology

For the maintenance of ship equipment, the fault location or fault diagnosis is the key. Through the accumulation of long-term fault data and the continuous growth of maintenance personnel's maintenance experience, the efficiency of fault diagnosis is constantly improving, but the fault data is mostly stored in paper form. It is very inconvenient to check, and the uneven experience level of maintenance personnel also affects the efficiency of fault diagnosis

Most of the existing fault diagnosis systems use simple text mode for fault management and diagnosis, and do not have the function of self-learning. Therefore, a graphical fault diagnosis system that can learn by itself and has graphic interaction functions is particularly important.

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