Fault diagnosis method for flow passage of steam turbine

Abstract

The invention belongs to the field of industrial monitoring, particularly relates to the application of an empirical mode decomposition method and a probabilistic neural network in the aspect of fault diagnosis of flow passages of heat-engine plant steam turbines, and provides a fault diagnosis method for a flow passage of a steam turbine on the basis of empirical mode decomposition and the probabilistic neural network. The method includes the steps that firstly, fault symptom parameter data of the flow passage are collected when the heat-engine plant steam turbine operates normally and has different faults, fault features are extracted from the corresponding symptom parameter data by the utilization of the advantages of empirical mode decomposition in processing non-stationary and nonlinear data, fault detection and recognition are carried out by the utilization of the powerful nonlinear mode classification performance of the probabilistic neural network, and then faults of the flow passage of the steam turbine are effectively diagnosed. By means of the fault diagnosis method, the faults of the flow passage of the heat-engine plant steam turbine can be diagnosed rapidly and accurately.

Description

technical field [0001] The invention belongs to the field of industrial monitoring, and specifically relates to the application of an empirical mode decomposition method and a probabilistic neural network in the fault diagnosis direction of the flow part of a steam turbine in a thermal power plant. Background technique [0002] Turbine generator set is the main equipment of electric power production enterprises, and it will cause huge economic loss whether it is shut down for failure or shut down for maintenance. The fault diagnosis of the flow part of the steam turbine is of great significance to the safe and economical operation of the steam turbine itself. On the one hand, the potential safety hazards of the unit can be eliminated through fault detection, and on the other hand, the overhaul period of the unit can be appropriately extended, so that the unit's economy can be improved while ensuring the safety of the unit's operation. Therefore, it is particularly important...

AI Technical Summary

Problems solved by technology

The Empirical Mode Decomposition method has been widely used in various fields since it was proposed in 1998, and has achieved good results, but it has not been applied to the field of fault diagnosis of the flow part of the steam turbine.

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