Ship electric power station fault diagnosing method based on knowledge petri network

Abstract

The invention discloses a ship electric power station fault diagnosing method based on a knowledge petri network. The method includes the steps that (1) fault symptom sets of units of a ship power station are obtained and screened according to a ship power station fault Petri network model; (2) by means of an improved Apriori algorithm, strong association rule mining is carried out on the fault symptom sets and the fault units; (3) by means of man-machine conversation, a user inputs fault symptom characteristic quantity and confidence, a system carries out fault symptom identification through fuzzy reasoning by means of a strong association rule to determine the fault units; (4) the fault units serve as a root database, faulty Petri subnets are extracted from the Petri network model, fault reason diagnosis is carried out by means of a forward operation and backward inference method, and according to diagnosis results, fault reasons, fault route graphs and a corresponding fault maintenance method are provided. The ship electric power station fault diagnosing method can avoid false negatives of the fault reasons, generates a fault propagation path, and improves accuracy and efficiency of ship power station fault diagnosis.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis of a marine power station, in particular to a method for fault diagnosis of a marine power station based on a knowledge-based petri (Petri net is a mathematical representation of a discrete parallel system) network. Background technique [0002] There are many equipments in marine power stations, the technologies involved are complicated, and the maintenance work is very heavy. Although the efficiency of power station fault diagnosis is constantly improving through long-term data accumulation and the continuous growth of maintenance personnel's maintenance experience, the fault data is mostly stored in paper form, and no fault knowledge has been formed. At the same time, the experience level of maintenance personnel is uneven. It also affects the efficiency of fault diagnosis. [0003] The current computer fault diagnosis technology mainly adopts methods such as wavelet transform, least s...

AI Technical Summary

Problems solved by technology

The disadvantage of Petri nets is that the number of nodes increases with the increase of system complexity, which makes model analysis difficult and easily causes state space explosion.

For the fault diagnosis of large and complex equipment, the system has a very large amount of information and has relatively large redundancy, which will affect the accuracy and efficiency of Petri net modeling to a certain extent.

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