Meng Liang, Su Yuanhao, Kong Xiaojia, Lan Xiaosheng, Li Yunfeng, Xu Tongle, Ma Jinying
School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China.
Sensors (Basel). 2022 Oct 9;22(19):7644. doi: 10.3390/s22197644.
The technology of fault diagnosis helps improve the reliability of wind turbines. Difficulties in feature extraction and low confidence in diagnostic results are widespread in the process of deep learning-based fault diagnosis of wind turbine bearings. Therefore, a probabilistic Bayesian parallel deep learning (BayesianPDL) framework is proposed and then achieves fault classification. A parallel deep learning (PDL) framework is proposed to solve the problem of difficult feature extraction of bearing faults. Next, the weights and biases in the PDL framework are converted from deterministic values to probability distributions. In this way, an uncertainty-aware method is explored to achieve reliable machine fault diagnosis. Taking the fault signal of the gearbox output shaft bearing of a wind turbine generator in a wind farm as an example, the diagnostic accuracy of the proposed method can reach 99.14%, and the confidence in diagnostic results is higher than other comparison methods. Experimental results show that the BayesianPDL framework has unique advantages in the fault diagnosis of wind turbine bearings.
故障诊断技术有助于提高风力涡轮机的可靠性。在基于深度学习的风力涡轮机轴承故障诊断过程中，特征提取困难和诊断结果置信度低的问题普遍存在。因此，提出了一种概率贝叶斯并行深度学习（BayesianPDL）框架并实现了故障分类。提出了一种并行深度学习（PDL）框架来解决轴承故障特征提取困难的问题。接下来，将PDL框架中的权重和偏差从确定性值转换为概率分布。通过这种方式，探索了一种不确定性感知方法来实现可靠的机器故障诊断。以某风电场风力发电机齿轮箱输出轴轴承的故障信号为例，所提方法的诊断准确率可达99.14%，且对诊断结果的置信度高于其他对比方法。实验结果表明，BayesianPDL框架在风力涡轮机轴承故障诊断中具有独特优势。
