Method for diagnosing failure of wind-powered rotary support based on wavelet analysis

Abstract

The invention discloses a method for diagnosing a failure of a wind-powered rotary support based on wavelet analysis. The method is characterized by comprising the following steps of a) extracting an acceleration signal and a torque signal of the early failure of the wind-powered rotary support through an acceleration sensor and a torque sensor; b) transmitting the torque signal through a transmitter, and converting the transmitted torque signal and the acceleration signal through a current and voltage converting plate; c) selecting a proper wavelet basis function in an NI data acquisition module, and performing multiscale decomposition on a failure signal by a wavelet analysis method; d) extracting fine characteristics of the failure signal from each scale decomposition reconstruction waveform and a frequency spectrum of the scale decomposition reconstruction waveform; and e) determining the failure type or the time when the failure occurs. The acceleration signal and the torque signal serve as characteristic parameters for the first time, and the failure signal of the wind-powered rotary support is acquired, so that the traditional problem of limitation under the condition of low speed of a vibration signal is solved.

Description

technical field [0001] The invention relates to a fault diagnosis method for a slewing bearing, in particular to a fault diagnosis method for a low-speed, heavy-duty slewing bearing such as a wind power slewing bearing, and in particular to a fault diagnosis method for a wind power slewing bearing based on wavelet analysis. Background technique [0002] Wind power slewing bearings are generally large-scale slewing bearings, and large-scale slewing bearings have large mechanical dimensions (the diameter is usually 0.4-10 meters, and some diameters are as large as 40 meters), and the speed is low (usually below 30 rpm). It is heavy and bears dynamic loads. Since the wind turbine works in the high altitude (40~60m) environment in the field, and the slewing bearing works in the environment of wind, sand, rain, salt spray, humidity, etc., it brings inconvenience to its installation, lubrication and maintenance. For this reason, not only the slewing bearing is required to have su...

AI Technical Summary

Problems solved by technology

[0007] The invention provides a wind power slewing bearing fault diagnosis method based on wavelet analysis. Its main technical feature is that the acceleration signal and torque signal of the early fault are extracted through the acceleration sensor and the torque sensor, and the acceleration data collection, torque data collection, and noise suppression are mainly solved. , signal feature extraction, fault identification and location, etc.

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