稀疏表示自编码网络的齿轮平稳型故障特征提取研究  

Research on feature extraction of steady-type gear faults with sparse representation auto-encoder network

作　　者：郑琛 丁康[1] 何国林[1,2] 蒋飞 叶鸣 ZHENG Chen;DING Kang;HE Guolin;JIANG Fei;YE Ming(School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510640,China;Guangdong Artificial Intelligence and Digital Economy Laboratory(Guangzhou),Guangzhou 510640,China;Guangzhou Huangong Motor Vehicle Inspection Technology Co.,Ltd.,Guangzhou 510640,China)

机构地区：[1]华南理工大学机械与汽车工程学院,广州510640 [2]人工智能与数字经济广东省实验室,广州510640 [3]广州华工机动车检测技术有限公司,广州510640

出　　处：《重庆理工大学学报（自然科学）》2023年第1期101-110,共10页Journal of Chongqing University of Technology：Natural Science

基　　金：国家自然科学基金项目(52075182,51875207)。

摘　　要：受到噪声和设备偏心等因素的干扰,定轴齿轮平稳型故障的整体特征参数难以准确提取,而智能诊断方法提取的多为抽象特征,不具备可解释性。联合平稳型故障响应机理与稀疏表示理论,设计了具备可解释性的稀疏表示自编码网络,将自编码网络的编码层和解码层分别等效为稀疏向量的求解与过完备字典的学习;基于平稳型故障信号参数的特征设计了自适应优化算法,有效实现了特征参数的快速全局寻优;结合设计的稀疏表示自编码网络与齿轮平稳型故障信号特征构建了深度神经网络,对故障信号进行高精度的特征重构。仿真分析表明该方法特征提取精度高、抗噪性能好,能够直接提取具有明确物理意义的平稳型故障特征参数,进一步验证了所提方法的有效性。Due to the interference of noise, equipment eccentricity and other factors, it is hard to accurately extract the steady-type fault parameters of fixed-shaft gears. Additionally, the features extracted by intelligent diagnosis methods are often abstract and difficult to explain. To solve this problem, firstly, an interpretable sparse representation Auto-Encoder network is designed based on the steady-type fault response mechanism and sparse representation theory. The encoding and decoding layers of Auto-Encoder network are equivalent to the solution of the sparse vector and learning of the over complete dictionary respectively. Based on the characteristics of steady-type fault signal parameters, an adaptive optimization algorithm is then designed to realize the fast-global optimization of characteristic parameters. Combining the designed sparse representation Auto-Encoder network and the steady-type fault signal features of fixed-shaft gears, a deep neural network is built to achieve a high-precision feature reconstruction of steady-type fault signals. Finally, the simulation shows that the proposed method can directly extract steady-type fault feature parameters with a clear physical meaning, and has high feature extraction accuracy and good anti-noise performance, which further verifies the effectiveness of the proposed method.

关 键 词：定轴齿轮 特征提取 自编码网络 稀疏表示 平稳型故障 

分 类 号：TN911[电子电信—通信与信息系统]