Wind Turbine Condition Monitoring Based on Assembled Multidimensional Membership Functions Using Fuzzy Inference System

Abstract

Condition monitoring (CM) has been playing an important role in the operation and maintenance of industrial equipment. However, the changeable environments where some equipment, such as wind turbines (WTs) are installed, have put a negative effect on CM. To deal with this problem, this article proposes a CM method of WTs based on assembled multidimensional membership functions (MFs) using a fuzzy inference system. First, multidimensional MFs, which are formed by fusing environment factor into conventional MFs, are proposed to reduce the negative effect from changeable environments. Second, the input data are properly divided into segments and the segments are classified into four types to distinguish the effects of different data on CM. Different weights are assigned to each segment and the corresponding membership of each segment is calculated separately. Then, these memberships are assembled for fuzzy inference. Finally, based on the assembled multidimensional MFs, a new CM architecture is established. Four groups of experiments were carried out to evaluate the proposed method with the data collected from a wind farm in northern China. The experiments results show that the proposed method can not only detect anomalies at an early stage but also effectively decrease the false alarms and missing detections.