Fault early warning method of gearbox of wind turbine generator set

Abstract

The invention discloses a fault early warning method of a gearbox of a wind turbine generator. The early warning method mainly includes the following steps of data acquisition, wherein a terminal acquires the historical data of wind turbine generator power, environment temperature and wind speed through a hard contact temperature sensor, a soft contact temperature sensor and a temperature acquisition card arranged in the gearbox or calls the historical data of wind turbine generator power, environment temperature and wind speed from wind power plant recorded data, and the time span of the datacovers the whole fault evolution section; data processing, wherein input data is normalized, the integrity of the input data is verified, abnormal data and incomplete data are deleted from the inputdata, a BP neural network including an AIS-SA hybrid network prediction algorithm is established, and then the network size and various initial connection weights and thresholds are determined; earlywarning calculation, wherein the acquired temperature data is predicted through the optimized network, the wind speed is subjected to approximate substitution in combination with numerical weather prediction, a temperature prediction conversion curve in a certain future time is obtained, the residual of predicted temperature and actual temperature is calculated, and with several recording points as an early warning section, the residual mean and standard deviation of the two temperatures in the section are worked out.

Description

technical field [0001] The invention relates to the field of early fault diagnosis and early warning of wind turbine gearboxes, in particular to a fault early warning method for wind turbine gearboxes. Background technique [0002] The current research on gearbox faults mainly includes two aspects: analyzing by establishing a fault evolution model and using artificial intelligence algorithms to process fault signals. Especially in recent years, with the emergence of various professional sensors in other fields and the vigorous development of artificial intelligence algorithms, the use of computers for real-time monitoring of gearboxes can accurately diagnose the fault location, but it is very difficult to diagnose gearbox faults. There is little experience with successful application. [0003] At present, the research on gearbox faults is mostly in the field of fault diagnosis, that is, the initial fault is judged at the first time or the short-term prediction of serious fa...

AI Technical Summary

Problems solved by technology

Especially in recent years, with the emergence of various professional sensors in other fields and the vigorous development of artificial intelligence algorithms, the use of computers for real-time monitoring of gearboxes can accurately diagnose the fault location, but it is very difficult to diagnose gearbox faults. Little experience with successful application

[0003] At present, the research on gearbox faults is mostly in the field of fault diagnosis, that is, the initial fault is judged at the first time or the short-term prediction of serious faults, and it is impossible to predict the state of the equipment in advance and optimize the maintenance decision.

At the same time, the fault model is inaccurate, and various working conditions cannot be fully considered, the fault early warning time is short, and the fault index prediction accuracy is low.

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