Fault diagnosis method used for downhole draining system

Abstract

The invention discloses a fault diagnosis method used for a downhole draining system. Fault information is measured through a draining equipment data monitoring system, and a fault feature vector reflecting the fault information is obtained through preprocessing. Then, fuzzy processing is carried out on the fault feature vector, a fuzzy value adapting to a Petri net input feature is obtained, and a fuzzy Petri net fault diagnosis model of the draining system is established according to the fuzzy value. Then, a BP algorithm in a neural network is adopted on the fault diagnosis model to carry out network optimization training on parameters, such as weight, threshold and confidence, an error is minimized, and the optimized parameters are used as the final running parameters of the fault diagnosis model. By analyzing the embodiment, the method can relatively accurately and rapidly find fault reasons, has great accuracy, rapidness and adaptability, and can be used for diagnosing the fault of the downhole draining system and have a relatively better effect.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis, and in particular relates to a fault diagnosis method for an underground drainage system. Background technique [0002] Whether the operation status of the underground drainage system is normal or not is directly related to the performance of the underground drainage, and the performance of the underground drainage directly affects the safety of coal mine production. It is of great significance for the stable operation of the drainage system to detect and solve the fault problem of power electromechanical equipment early. [0003] At present, there are many methods for drainage system fault diagnosis at home and abroad, but each method has its own scope of application and limited conditions, and it is difficult to obtain good diagnostic results. The main methods are as follows: [0004] (1) Fault diagnosis method based on expert knowledge: [0005] The composition of an expert system g...

AI Technical Summary

Problems solved by technology

However, in the case of multi-dimensionality, the amount of data is huge, and it is difficult to calculate and solve; it needs to be established on the basis of a large number of sample libraries, that is, a large number of sample training is required, and the convergence speed is not good; the liberalization ability of the network is weak, and it is easy to fall into a local minimum point

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