Abstract
Condition monitoring (CM), fault diagnosis (FD), and nondestructive testing (NDT) are currently considered crucial means to increase the reliability and availability of wind turbines. Many research works have focused on CM and FD for different components of wind turbine. Gear is typically used in a wind turbine. There is insufficient space to locate the sensors for long-term monitoring of fatigue state of gear, thus, offline inspection using NDT in both manufacturing and maintenance processes are critically important. This paper proposes an inductive thermography method for gear inspection. The ability to track the properties variation in gear such as electrical conductivity, magnetic permeability, and thermal conductivity has promising potential for the evaluation of material state undertaken by contact fatigue. Conventional thermography characterization methods are built based on single physical field analysis such as heat conduction or in-plane eddy current field. This study develops a physics-based multidimensional spatial-transient-stage tensor model to describe the thermo optical flow pattern for evaluating the contact fatigue damage. A helical gear with different cycles of contact fatigue tests was investigated and the proposed method was verified. It indicates that the proposed methods are effective tool for gear inspection and fatigue evaluation, which is important for early warning and condition-based maintenance.
Highlights
W IND energy is one of the fastest growing renewable energy resources, and it is going to have remarkable share in the energy market [1], [2]
Inductive Thermography and Tensor In IT experiments, 2 s video were recorded by camera which includes 400-ms heating time followed by 1600 ms cooling time
Due to the property variation during the fatigue process, singular patterns of Thermal Optical Flow (TOF) appear at fatigue contact tooth flank and TOF converge on the fatigue affective areas
Summary
W IND energy is one of the fastest growing renewable energy resources, and it is going to have remarkable share in the energy market [1], [2]. As the sectors of wind energy grow, business economics will demand increasingly careful management of costs. The operations and maintenance (O&M) costs of wind turbines (WTs) account for about 25–30% of the overall energy generation cost or 75–90% of the investment costs [3]. In order to reduce the cost of wind energy, there is a pressing need to reduce the O&M cost [4]. Aside from developing more advanced machine designs to improve the availability, another effective way to achieve this improvement would be to apply reliable and cost-effective condition monitoring (CM), fault diagnosis (FD), nondestructive testing (NDT) [5], and structural.
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