| 王政博,王红军,张 翔,崔英杰,苏静雷.燃气轮机深度卷积生成对抗故障样本生成研究[J].电子测量与仪器学报,2022,36(6):82-90 |
| 燃气轮机深度卷积生成对抗故障样本生成研究 |
| Research on fault sample generation of gas turbine based ondeepconvolution generative countermeasures |
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| DOI： |
| 中文关键词: 燃气轮机 故障数据 故障诊断 生成对抗网络 |
| 英文关键词:gas turbine fault data fault diagnosis generative adversarial network |
| 基金项目:国家自然科学基金（51975058）项目资助 |
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| Author | Institution |
| Wang Zhengbo | 1. School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University,2. Beijing High-end Equipment Intelligent Perception and Control International Cooperation Base,3. Beijing Key Laboratory of Measurement and Control of Mechanical and Electrical System, Beijing Information Science & Technology University |
| Wang Hongjun | 1. School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University,2. Beijing High-end Equipment Intelligent Perception and Control International Cooperation Base,3. Beijing Key Laboratory of Measurement and Control of Mechanical and Electrical System, Beijing Information Science & Technology University |
| Zhang Xiang | 1. School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University,4. Tianjin Optoelectronic Communication Technology Co. , Ltd. |
| Cui Yingjie | 1. School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University,2. Beijing High-end Equipment Intelligent Perception and Control International Cooperation Base,3. Beijing Key Laboratory of Measurement and Control of Mechanical and Electrical System, Beijing Information Science & Technology University |
| Su Jinglei | 1. School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University,2. Beijing High-end Equipment Intelligent Perception and Control International Cooperation Base,3. Beijing Key Laboratory of Measurement and Control of Mechanical and Electrical System, Beijing Information Science & Technology University |
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| 中文摘要: |
| 针对应用深度学习进行燃气轮机故障诊断时,因故障信号数据不易获取,使得正常运行样本多、故障样本少,影响故障
诊断准确率的问题,提出了一种采用深度卷积生成对抗学习对燃气轮机故障样本进行扩充的方法。 根据燃气轮机振动信号特
点,利用快速傅里叶变换、经验模态分解、解调预处理故障信号,提取故障频域特征并选取特征值指标,将振动信号转为二维灰
度图像,通过正交梯度惩罚算法训练深度卷积生成对抗故障样本生成模型。 实例结果表明,使用所提方法获得 CWRU 轴承数
据集生成样本测试准确率为 98. 01%;某型燃气轮机生成样本测试准确率为 97. 43%,同条件下均优于其他主流故障样本生成方
法,验证了所提故障样本生成方法的有效性和优越性。 |
| 英文摘要: |
| Aiming at the problem that when applying deep learning for gas turbine fault diagnosis, the fault signal data is difficult to
obtain, resulting in many normal operation samples and few fault samples, which affect the accuracy of fault diagnosis. A method for
augmenting gas turbine fault samples using deep convolutional generative adversarial learning is proposed. According to the
characteristics of the gas turbine vibration signal, the fault signal is preprocessed by using fast Fourier transform, empirical mode
decomposition and demodulation, and the fault frequency domain features are extracted and the eigenvalue index is selected, and the
vibration signal is converted into a two-dimensional gray image. The orthogonal gradient penalty algorithm is used to train the deep
convolutional generative adversarial fault sample generation model. The example results show that the test accuracy rate of CWRU bearing
dataset obtained is 98. 01%, and the test accuracy rate of a certain type of gas turbine’s fault samples generated by the proposed method
is 97. 43%, which are better than other mainstream fault sample generation methods under the same conditions. The effectiveness and
superiority of the proposed fault sample generation method are verified. |
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