State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China.
College of Mechanical Engineering, Chongqing University, Chongqing 400044, China.
Sensors (Basel). 2020 Apr 20;20(8):2339. doi: 10.3390/s20082339.
The gearbox is one of the most fragile parts of a wind turbine (WT). Fault diagnosis of the WT gearbox is of great importance to reduce operation and maintenance (O&M) costs and improve cost-effectiveness. At present, intelligent fault diagnosis methods based on long short-term memory (LSTM) networks have been widely adopted. As the traditional softmax loss of an LSTM network usually lacks the power of discrimination, this paper proposes a fault diagnosis method for wind turbine gearboxes based on optimized LSTM neural networks with cosine loss (Cos-LSTM). The loss can be converted from Euclid space to angular space by cosine loss, thus eliminating the effect of signal strength and improve the diagnosis accuracy. The energy sequence features and the wavelet energy entropy of the vibration signals are used to evaluate the Cos-LSTM networks. The effectiveness of the proposed method is verified with the fault vibration data collected on a gearbox fault diagnosis experimental platform. In addition, the Cos-LSTM method is also compared with other classic fault diagnosis techniques. The results demonstrate that the Cos-LSTM has better performance for gearbox fault diagnosis.
变速箱是风力涡轮机 (WT) 中最脆弱的部件之一。WT 变速箱的故障诊断对于降低运营和维护 (O&M) 成本并提高成本效益非常重要。目前，基于长短时记忆 (LSTM) 网络的智能故障诊断方法已被广泛采用。由于 LSTM 网络的传统 softmax 损失通常缺乏判别力，因此本文提出了一种基于余弦损失 (Cos-LSTM) 的风力涡轮机变速箱故障诊断方法。余弦损失可以将损失从欧几里得空间转换到角空间，从而消除信号强度的影响，提高诊断精度。利用振动信号的能量序列特征和小波能量熵来评估 Cos-LSTM 网络。利用齿轮箱故障诊断实验台上采集的故障振动数据验证了所提方法的有效性。此外，还将 Cos-LSTM 方法与其他经典故障诊断技术进行了比较。结果表明，Cos-LSTM 方法在变速箱故障诊断方面具有更好的性能。
