Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

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
View PDF5 Cites 98 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

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 application effect is not ideal; and the system can only diagnose certain types of faults, which limits the scope of the fault diagnosis function of the system Complete

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • 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

Examples

Experimental program
Comparison scheme
Effect test

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...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

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

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
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
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com