基于AHP-EWM权重与模糊评判的风机齿轮箱健康评价方法

Evaluation Method for Wind Turbine Gearbox Health Based on AHP-EWM Weight and Fuzzy Evaluation

针对传统故障诊断算法依据人工经验判断、对数据挖掘不深等问题,提出使用模糊评判T1FL法将齿轮箱系统的综合评价划分为4个健康状态。为了科学地赋权各级状态指标所占的健康权重,构建以最小二乘法耦合改进的层次分析法(AHP)和改进的熵权法NBN-EWM的数字化权重计算模型。结果表明:所提方法以多源数据为输入,通过最大隶属度原则综合评估齿轮箱的健康状态,诊断出齿轮箱部件高速轴处存在0.9201的置信空间为齿轮断齿状态,对4种健康状态的评估准确率分别保持在99%、99.2%、98.2%与99.6%。所提方法能够应用于齿轮箱的早期故障诊断,量化评判齿轮箱部件的健康状态,精确定位齿轮箱的损伤部位,为机组运维策略提供安全性保障,并为风场的能源数字化、设备智能健康与运维提供新方案。

In view of the problems of traditional fault diagnosis algorithms based on manual experience and poor data mining,the T1FL fuzzy evaluation method was used to divide the comprehensive evaluation of gearbox system into four health states.In order to scientifically empower the health weight occupied by all levels of state indicators,the digital weight calculation model of least squares coupled enhanced analytic hierarchy process method(AHP)and the improved entropy weight method NBN-EWM was constructed.The results show that the proposed method comprehensively evaluates the gearbox′s health state through the maximum membership principle with the input of multi-source data.A broken gear condition within a 0.9201 confidence space at the high-speed shaft of the gearbox component is diagnosed,and the evaluation accuracy for the four health statuses is 99%,99.2%,98.2%and 99.6%,respectively.The proposed method can be applied to the early fault diagnosis of gearbox,quantify the health status of gearbox components,accurately locate the damage parts of gearbox,which can provide security guarantee for unit operation and maintenance strategy,and provide new solutions for energy digitalization,intelligent health and operation and maintenance of wind field.