Fan gear box space failure predicating method based on temperature data

Abstract

The invention discloses a fan gear box space failure predicating method based on temperature data. The method comprises the following steps of A1, temperature data preprocessing: a regression analysis method is utilized for carrying out single-step predication on temperature data, a difference value between a practical value and a predicating value is obtained and is called as a residual, and the residual is used as the observing quantity Y of a random state space model; A2, random state space model recognition; A3, gear box failure predication. The fan gear box space failure predicating method has the advantages that through analyzing internal features, reflected by the temperature data, of a gear box, early warning signals can be sent out at the initial failure stage with low temperature, the damage to the gear box can be reduced, and the occurrence of irreversible failure is avoided.

Description

technical field [0001] The invention relates to a temperature data-based fault prediction method for a fan gear box space. Background technique [0002] Among the many failure types of wind turbines, although the failure rate of the gearbox is relatively low, the downtime caused by its failure is the longest and the economic loss is the largest. At present, the fault detection methods for wind turbine gearbox mainly include: vibration signal-based method, noise signal-based method, oil analysis method, acoustic emission detection technology, etc., but the sensors for the above methods on wind turbines have not yet been popularized. Domestic wind turbines have realized the temperature monitoring of gearboxes. Therefore, from the perspective of existing technology and economy, directly using temperature signals to realize the fault prediction of gearboxes has its specific advantages. The occurrence of most failures is a gradual process, such as failure of bearings and gears, ...

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Problems solved by technology

[0002] Among the many failure types of wind turbines, although the failure rate of the gearbox is relatively low, the downtime caused by its failure is the longest and the economic loss is the largest

At present, the fault detection methods for wind turbine gearbox mainly include: vibration signal-based method, noise signal-based method, oil analysis method, acoustic emission detection technology, etc., but the sensors for the above methods on wind turbines have not yet been popularized. Domestic wind turbines have realized the temperature monitoring of gearboxes. Therefore, from the perspective of existing technology and economy, directly using temperature signals to realize the fault prediction of gearboxes has its specific advantages.

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