基于多时间尺度的风电机组齿轮箱早期故障监测方法

张 程; 雷步辰; 余士霖; 陆万林; 陈昌亮

doi:10.19783/j.cnki.pspc.260282

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基于多时间尺度的风电机组齿轮箱早期故障监测方法

应用研究 | 更新时间：2026-09-28

- 基于多时间尺度的风电机组齿轮箱早期故障监测方法
- A multi-timescale-based early fault monitoring method for wind turbine gearboxes
- 电力系统保护与控制 2026年54卷第19期
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- DOI：10.19783/j.cnki.pspc.260282
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- 纸质出版：2026
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张程, 雷步辰, 余士霖, 等. 基于多时间尺度的风电机组齿轮箱早期故障监测方法[J]. 电力系统保护与控制, 2026,54(19).

ZHANG Cheng, LEI Buchen, YU Shilin, et al. A multi-timescale-based early fault monitoring method for wind turbine gearboxes[J]. Power System Protection and Control, 2026, 54(19).
张程, 雷步辰, 余士霖, 等. 基于多时间尺度的风电机组齿轮箱早期故障监测方法[J]. 电力系统保护与控制, 2026,54(19). DOI： 10.19783/j.cnki.pspc.260282.

ZHANG Cheng, LEI Buchen, YU Shilin, et al. A multi-timescale-based early fault monitoring method for wind turbine gearboxes[J]. Power System Protection and Control, 2026, 54(19). DOI： 10.19783/j.cnki.pspc.260282.

摘要

针对风电机组数据采集与监视控制系统(supervisory control and data acquisition

SCADA)数据中早期故障特征微弱且运行状态多变导致故障识别困难的问题，提出了一种基于并行门控循环单元(parallel gated recurrent unit

P-GRU)与多时间尺度指数加权移动平均(multi-time-scale exponentially weighted moving average

MTS-EWMA)的风电机组齿轮箱早期故障监测方法。首先，利用重叠滑动窗口采样(overlapping sliding window sampling

OSWS)策略对SCADA多变量时间序列进行重构，以充分保留时序信息。然后，构建P-GRU预测模型，通过并行时序特征提取结构增强模型对复杂运行工况下多变量动态关联特征的学习能力。然后，在此基础上构建MTS-EWMA监测框架，通过融合不同时间尺度的预测残差信息，实现对缓慢演化故障的敏感检测与误报抑制。实验结果表明，所提方法能够在风电机组实际故障发生前实现稳定预警，验证了该方法在风电机组早期故障监测中的有效性。

Abstract

To address the challenges of fault identification in wind turbines caused by weak early fault features and variable operating conditions in SCADA data

this paper proposes an early fault monitoring method for wind turbine gearboxes based on parallel gated recurrent unit (P-GRU) and multi-timescale exponentially weighted moving average (MTS-EWMA). First

an overlapping sliding window sampling (OSWS) strategy is employed to reconstruct the SCADA multivariate time series to fully preserve temporal information. Subsequently

a P-GRU prediction model is constructed

and through its parallel temporal feature extraction architecture

the model’s capability to learn multivariate dynamic correlation features under complex operating conditions is enhanced. Finally

an MTS-EWMA monitoring framework is constructed to fuse prediction residuals from different time scales

thereby enabling sensitive detection of slowly evolving faults while suppressing false alarms. Experimental results demonstrate that the proposed method can provide stable early warnings before actual wind turbine faults

thereby verifying its effectiveness for early fault monitoring of wind turbine gearboxes.

关键词

Keywords

references

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