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

A marine power station and fault diagnosis technology, applied in the direction of fault location, electrical digital data processing, special data processing applications, etc., can solve problems such as increased number of nodes, large amount of information, and difficult model analysis

Active Publication Date: 2015-01-07
NAVAL UNIV OF ENG PLA
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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 diagnosi

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

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Embodiment Construction

[0047] Below in conjunction with accompanying drawing and specific embodiment the present invention is described in further detail:

[0048] A kind of fault diagnosis method of marine power station based on knowledge petri net, it is characterized in that, it comprises the steps:

[0049] Step 1: Obtain the set of actual faults and corresponding fault symptoms of each unit of the marine power station in the existing marine power station fault Petri net model Σ;

[0050] Step 2: Use the improved Apriori algorithm described in the following steps 201 to 205 for the set of actual faults and corresponding fault symptoms of each unit of the above-mentioned marine power station to mine strong association rules, that is, the fault symptoms and the corresponding fault symptoms of each unit of the marine power station Strong association rules between actual faults are mined, such as figure 1 shown;

[0051] Step 201: Set the set of actual faults and corresponding fault symptoms of ea...

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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...

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Application Information

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IPC IPC(8): G06F19/00G01R31/08
Inventor 马良荔王燕平孙煜飞苏凯覃基伟
Owner NAVAL UNIV OF ENG PLA
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