Method for predicting and diagnosing fault of a gearbox of wind turbine generator

A technology for fault prediction and wind turbines, which is applied in the monitoring of wind turbines, testing of wind turbines, and mechanical components, etc. It can solve problems that affect the accuracy and speed of fault diagnosis, over-adaptation, poor convergence speed, etc.

Pending Publication Date: 2021-12-03
HUANENG NEW ENERGY CO LTD +1
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However, the neural network is prone to fall into the local minimum problem, and there will be over-adaptation phenomenon.
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  • Method for predicting and diagnosing fault of a gearbox of wind turbine generator
  • Method for predicting and diagnosing fault of a gearbox of wind turbine generator
  • Method for predicting and diagnosing fault of a gearbox of wind turbine generator

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[0034] It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be further described in detail in conjunction with the accompanying drawings and specific embodiments below.

[0035] Such as figure 1 As shown, a method for fault prediction and diagnosis of wind turbine gearboxes, which uses the ARIMA model to predict the fault trend of wind turbine gearboxes, and this method deals with the monitoring data of gearbox operating conditions with nonlinear and non-stationary characteristics, Predict trend changes of condition monitoring time series data with a model based on autocorrelation analysis of time series.

[0036] The time series data obtained by the ARIMA model through experiments or production site collection first needs to be tested for stationarity and white noise before it can be used for model analysis, then model identification, pa...

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Abstract

The invention discloses a method for predicting and diagnosing a fault of a gearbox of a wind turbine generator, which comprises the following steps of: revealing certain significant relevance among data by establishing an ARIMA (Autoregressive Integrated Moving Average)-based time sequence model for continuously changing state monitoring data, and carrying out continuation on historical data according to the time sequence model. Therefore, the operation condition of a complex mechanical system is obtained by predicting the state trend, an ARIMA model is established for historical sample data of the outlet pressure of the gearbox, the main change trend of the short-term outlet pressure state can be predicted, the prediction precision is satisfactory, and a certain reference value is provided for fault state analysis of the gearbox, due to the fact that prediction deviation of local sampling points can be large due to the change degree of single state monitoring data and the model parameter suitability degree, relevant parameters need to be corrected, and the accuracy and reliability of fault trend prediction need to be further improved together with other continuously-changing index data.

Description

technical field [0001] The invention relates to the field of fault diagnosis of wind turbines, in particular to a method for fault prediction and diagnosis of a gearbox of a wind turbine. Background technique [0002] As the key mechanical component of wind power generation equipment, the main shaft bearing of the wind turbine has high maintenance cost and long maintenance time. Abnormalities generated during the non-maintenance period are likely to cause the main shaft bearing to fail. The traditional SCADA system cannot timely and accurately locate hidden troubles. , affects the normal operation of wind turbines and the stability of wind power grid connection, so it is necessary to conduct in-depth research on the real-time operating status and fault diagnosis of wind turbine spindle bearings. [0003] The traditional spindle bearing fault diagnosis method is based on the vibration signal of the spindle bearing in the running state to carry out pattern recognition on the g...

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Application Information

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IPC IPC(8): G06F30/20G01M13/021F03D17/00G06F113/06G06F119/10
CPCG06F30/20G01M13/021F03D17/00G06F2119/10G06F2113/06Y02P70/50
Inventor 邓巍张晓朝屠劲林王雪璐王森冯笑丹汪臻刘腾飞郭靖张轶东
Owner HUANENG NEW ENERGY CO LTD
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