Zhejiang University

Zhenyu Liu

Jianrong Tan

Jin Cheng

Hao Lv

Feng Tang

Weifei Hu

Minglong Yang

Deshang Peng

Daxin Liu

Yaxuan Zhang

A method and a device for fault diagnosis of wind turbine pitch bearing based on neural network, the method includes: measuring the signal strength at different points of a sensor and different rolling angles, determining an optimal measurement rolling angle of a blade and sensor point arrangement, blocking the blade at the optimal rolling angle to collect the pitch vibration data, further processing the collected vibration data into a dataset, constructing a neural network model, using the collected dataset to train the network, and deploying the trained network to PLC for real-time dynamic monitoring of the wind turbine; the device includes the vibration acceleration sensors, a vibration data acquisition card and a programmable logic controller (PLC). The present disclosure can realize the fast, real-time and accurate monitoring of the health status of the pitch bearing.

Method and device for fault diagnosis of wind turbine pitch bearing based on neural network

A difference analysis method for 3D CAD models based on key-point matching. According to the method, a 3D CAD model is firstly transformed into a key-point representation, then a graph representation and a graph matching method are introduced to establish a pre-matching of vertices between the two 3D CAD models, and rigid transformation parameters between the two 3D CAD models are obtained through a mismatch elimination method; and subsequently, a matching relationship of key-points is established by checking their distance after rigid transformation to obtain a key-point matching sequence for hierarchical comparison, so as to identify the differences between vertices, edges, faces and solid models, thereby quickly and accurately identifying the difference between the 3D CAD models.

Difference analysis method for 3D cad models based on key-point matching

In a method for designing a high-rigidity blade based on stochastic isogeometric analysis, the models of stochastic fields of the material property and the external load of the blade are established based on manufacturing conditions and service environment of the blade; an optimization model of the blade is established according to high-rigidity design requirements of the blade and a constraint condition of lift-drag ratio, which is then solved. In the solution procedure, a stochastic isogeometric analysis method is used to calculate the stochastic displacement of the blade under the influence of the randomness of the material property and the external load, and the maximum lift-drag ratio of the blade airfoil is also calculated, based on which the fitness values of individuals in the current population are calculated, so that the high-rigidity design of the blade is realized in the premise of ensuring the lift-drag ratio.

Method for designing high-rigidity blade based on stochastic isogeometric analysis

A reliability-based topology optimization design method for a part structure considering bounded hybrid uncertainties, includes the following steps: considering the uncertainties in the manufacture and service of the part structure, describing an external load with insufficient samples and a material property with sufficient samples as an interval variable and a bounded probabilistic variable respectively; discretizing a design domain of the part structure, setting the physical and geometric constraints, and establishing a reliability-based topology optimization design model; solving by a moving asymptote algorithm: decoupling the probabilistic and interval uncertainties, and determining the worst working condition by using gradients of constraint performance functions; defining a performance fluctuation under the worst working condition and calculating reliability of constraint performance; and finally, calculating the gradients of objective and constraint functions with respect to the design variables for iteration.

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Hangzhou, China

Zhenyu Liu