Wind power gear box fault diagnosis test platform based on multi-feature fusion and method

Abstract

The invention discloses a wind power gear box fault diagnosis test platform based on multi-feature fusion and a method, solves the problems such as single detection method, a random wind load specialworking condition as well as oil change interference which are faced by wind power planet gear box fault diagnosis and has the beneficial effect of high fault diagnosis accuracy. A scheme is as follows: the test platform comprises a test bed base provided with a to-be-tested wind power gear box, a vibration acceleration sensor and a noise signal sensor are arranged at the circumference side of theto-be-tested wind power gear box, the circumference side of the to-be-tested wind power gear box is connected with an oil liquid information detection sensor, each sensor is respectively and independently connected with a data acquisition module, and an evaluation method can extract vibration feature indexes specific to random wind load working conditions, extract noise feature indexes and extract oil liquid feature indexes specific to oil change interference problems, so that a vibration-noise-oil liquid feature fusion evaluation model based on deep learning and DS evidence theory is built.

Description

technical field [0001] The invention relates to the technical field of gearbox detection, and more specifically relates to a multi-feature fusion-based fault diagnosis test platform and method for a speed-up gearbox of a wind power gear. Background technique [0002] With the large-scale construction of wind farms, wind turbines have entered a period of frequent failures. Most of the wind farms are located in remote areas, the distance between the wind turbines is relatively long, and the wind turbine body is tall, so it is not convenient to carry out equipment inspections, and the cost of shutdown and maintenance is also very high. Therefore, most of the current wind farms are implemented every six months. permanent maintenance. At present, the fault warning and diagnosis methods of wind turbines mainly rely on the SCADA system in the main monitoring room of the wind farm. According to the large amount of data collected, all the wind turbines in the wind farm can be centra...

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Problems solved by technology

[0008] In addition, the present invention provides a wind power planetary gearbox fault detection and evaluation method based on vibration-noise-oil feature fusion. This multi-feature information fusion fault detection and evaluation method can extract vibration characteristic indicators for random wind load conditions. , extract the noise feature index, and extract the oil feature index for the oil change interference problem, thus establishing a vibration-noise-oil feature fusion evaluation model based on deep learning and DS evidence theory, which is conducive to improving the fault diagnosis of wind power planetary gear gearboxes The comprehensiveness, intelligence and accuracy of

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