基于节点优化型DAG-LDM的机组滚动轴承故障诊断方法

滚动轴承作为风电机组的关键部件,对于整个机组的安全运行起着决定性作用.针对机组滚动轴承故障诊断问题,提出一种节点优化型有向无环图大间隔分布机(O-DAG-LDM)的故障诊断方法.结合DAG多分类扩展性能与LDM二分类器泛化性能的优点,构建一种面向滚动轴承故障诊断的DAG结构扩展式LDM多分类器方法.在DAG-LDM算法框架下,利用优化算法对DAG节点进行优化排列以减小随机排布引起的累积误差,提高LDM故障分类准确率.实验表明,与其他主流智能诊断方法相比,所提出的节点优化型DAG-LDM故障诊断方法具有较高的准确率和更好的抗噪性能.

Fault diagnosis method of wind turbine rolling bearing based on node optimized DAG-LDM

As a key component, the rolling bearing plays a decisive role in the safe operation of the whole wind turbine. To solve the problem of fault diagnosis in rolling bearings, a fault diagnosis method based on optimized directed acyclic graph combing with large margin distribution machine (O-DAG-LDM) is proposed. Combining the advantages of DAG multi-class scalable features with the generalization performance of LDM two-classifier, a DAG structure extended LDM multiple classifier method for rolling bearing fault diagnosis is constructed. In the framework of the DAG-LDM method, a node optimization algorithm is used to optimize the DAG nodes to reduce the cumulative error caused by random permutation, and improve the accuracy of LDM fault classification. The experiment shows that the proposed O-DAG-LDM method for fault diagnosis has higher accuracy and better capability of anti-noise immunity in comparison with other mainstream intelligent diagnosis methods.