风力发电机齿轮箱优化逐层故障诊断方法

Optimized hierarchical diagnostic approach for wind turbine gearbox

风力发电机齿轮箱的故障诊断在风力发电机组正常运行中起着重要作用,除了识别故障类型外,故障的严重程度对风机的维护也具有指导意义,因此,一种优化堆叠诊断结构(OSDS)被提出以识别故障类型和严重性。首先对原始振动信号进行压缩采样,然后将压缩样本分别输入第1层和第2层深度信任网络(DBN),对故障类型和严重性进行识别,同时采用混沌量子粒子群优化算法(CQPSO)对每个DBN进行优化。通过两组实验得到的结果表明,故障类型诊断准确率分别达到99.24%和97.21%,故障严重程度诊断准确率达到99.06%,同时诊断时间仅为1.493和2.176 s。

Fault diagnosis for gearbox of wind turbine plays an important role in the normal operation of WT.Current studies commonly focus on diagnosis of fault types,nevertheless,in addition to identifying the fault type,the severity of the fault is also instructive for maintenance and repair for wind turbine.Thus,a novel optimized stacked diagnosis structure(OSDS)is proposed for identification of fault type and severity.Compressed sensing is adopted to implement compressed sampling of original vibration signals.Then,compressed samples are input into first and second layer deep belief networks(DBNs)for identification of fault type and severity,separately.In addition,every single DBN in the OSDS is optimized with chaotic quantum particle swarm optimization(CQPSO)algorithm.Comparison experiments based on bench mark gearbox fault data and working planetary gearbox show that the fault type diagnosis accuracy of this method reaches 99.24%and 97.21%,while the fault severity accuracy reaches 99.06%.Meanwhile,the testing times are only 1.493 and 2.176 s.