Vibration monitoring-based wind generator set automatic fault diagnosis method

A technology for automatic diagnosis of wind turbines, applied in the direction of engine testing, measuring devices, testing of machine/structural components, etc., can solve problems such as limitations, inability to fully reflect time-varying characteristics of signals, and unsatisfactory application effects, etc., to achieve The effect of avoiding data loss and perfecting the fault diagnosis function

Inactive Publication Date: 2010-10-13
ZHEJIANG UNIV
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Problems solved by technology

Traditional methods based on stationary signal processing (such as spectrum analysis) cannot fully reflect the time-varying characteristics of the signal, so the app

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  • Vibration monitoring-based wind generator set automatic fault diagnosis method
  • Vibration monitoring-based wind generator set automatic fault diagnosis method
  • Vibration monitoring-based wind generator set automatic fault diagnosis method

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Embodiment Construction

[0023] Traditional fault diagnosis methods (including frequency domain analysis method and time domain analysis method) have a good diagnostic effect on distributed faults of mechanical components. But when a local fault occurs, the measured mechanical vibration signal will contain non-stationary or time-varying components. Traditional methods based on stationary signal processing (such as spectrum analysis) cannot fully reflect the time-varying characteristics of the signal, so the application effect is not ideal. Wavelet analysis is a time-frequency localized analysis method in which both the time window and the frequency window can be changed, and it is especially suitable for the analysis of non-stationary signals. For example, when faults such as gear pitting, broken teeth, slight bending of shafts, and fatigue spalling of rolling bearings occur, periodic pulse impact forces will be generated, resulting in modulation of vibration signals, which is manifested in the freque...

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Abstract

The invention discloses a vibration monitoring-based wind generator set automatic fault diagnosis method. The method comprises the following steps: an acceleration sensor collects original vibration signals, a pre-communication subsystem stores and uploads data, and a central monitoring server receives data and stores the data in a database. The method also comprises the step of establishing a fault characteristic frequency library in the database which is used as the basis of fault diagnosis. The method of the invention can automatically realize the fault early warning and fault diagnosis of the key parts of the wind generator set; and the fault characteristic frequency pool can be continuously updated to realize the self-learning function of the method, thus improving the fault diagnosis function continuously.

Description

technical field [0001] The invention relates to a fault diagnosis method, in particular to a vibration monitoring-based automatic fault diagnosis method for a wind power generating set with self-learning capability. Background technique [0002] Wind energy is a clean and renewable energy source. By the end of 2009, my country's new wind power installed capacity reached 13 billion watts. Since wind farms are generally located in harsh environments and the working conditions are extremely unstable, it is extremely necessary to monitor the operating status of wind turbines online and understand the operating status of key components of wind turbines in real time. In addition, changing regular maintenance to on-demand maintenance can greatly reduce the maintenance cost of wind farms. [0003] At present, some general condition monitoring systems have been applied to the field of wind power generation, but the analysis and diagnosis functions of the system are relatively weak. ...

Claims

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Application Information

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IPC IPC(8): G01H17/00G01M15/00
Inventor 颜文俊郭艳平王超包哲静郑军孟濬
Owner ZHEJIANG UNIV
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