| 何怡刚,鲁 力,阮 义,袁伟博.风力发电机齿轮箱优化逐层故障诊断方法[J].电子测量与仪器学报,2022,36(1):89-97 |
| 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 |
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| DOI： |
| 中文关键词: 风力发电机 齿轮箱 故障诊断 逐层诊断网络 |
| 英文关键词:wind turbine gearbox fault diagnosis hierarchical diagnostic network |
| 基金项目:国家自然科学基金（51577046）、国家自然科学基金（51977161）、国家自然科学基金（51977153）、国家自然科学基金重点项目（51637001）、国家重点研发计划“重大科学仪器设备开发”项目（2016YFF0102200）、装备预先研究重点项目（41402040301）资助 |
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| 中文摘要: |
| 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。 |
| 英文摘要: |
| 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. |
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