大DN值航空发动机轴承隐冲击模式的层级稀疏学习诊断方法研究

Aero-engine bearings with large DN value (The product of the bearing pitch diameter multiplied by the rotational speed over 1.0×10^6mm∙r/min) always generate overlapping and distorted response waveforms when faults exist. Meanwhile, the waveforms，which doesn’t have quasi-periodic damping pattern, are mixed by non-Gaussian noises and modulated by nonlinear compound transfer path, resulting in a special waveform phenomenon coined by Latent Impulsive Pattern (LIP). However, current bearing diagnosis methods is ineffective for feature signals with LIP, and thus it is very necessary to develop a new algorithmic framework to extract weak LIP features for reducing maintenance cost and avoiding catastrophic accidents..Therefore, based on the inversion procedure of the LIP feature propagation mechanism of aero-engine bearings, one type of hierarchical sparsity learning diagnosis technique is compressively developed in this project by establishing a multi-layer sparsity learning framework. The main contents to be focused include: firstly, adaptive enhancement algorithms are developed for LIP features under strong Non-Gaussian noises, secondly, regularized prior description is learned for LIP features, lastly, sparsity based deconvolution methods are designed for LIP features to cancel the modulation influence of nonlinear compound transfer path. Based on these works implemented in this project, a novel noise cancellation theory is established profoundly regardless of actual and reliable noise distribution models, and an inspiring data-driven route is also proposed for capturing the intrinsic structure of complex feature components, moreover, an elaborated multi-layer approximation mechanism composed of linear convolution and nonlinear function is revealed for nonlinear compound transfer path models. Lastly, integrating all proposed models and algorithms, one universal, reliable and comprehensive diagnostic framework is established to provide theoretical and technical supports for aero-engine safety operations.

大DN值（内径×转速高达1.0×10^6mm∙r/min）航空发动机轴承故障动态响应由于高速工况、非高斯噪声干扰和非线性复合传递路径调制而呈现出混叠的隐冲击模式，不再表征为明晰的准周期衰减冲击模式，制约了现有轴承诊断方法的有效性，亟需研究大DN值轴承故障诊断新框架，以降低维护成本并避免灾难性事故。.本项目基于大DN值轴承隐冲击特征源传播的逆过程，通过搭建多层稀疏学习的架构，开展层级稀疏学习诊断方法研究，具体包括：非高斯噪声干扰下的隐冲击特征自适应增强、隐冲击特征的正则化学习和非线性复合传递路径调制下的隐冲击特征稀疏解卷。通过本项目的研究，揭示不依赖精确噪声模型的非高斯噪声抑制新原理，构建数据驱动的复杂特征先验正则学习新机制，建立多级线性卷积和非线性函数联合逼近非线性复合传递路径调制的新策略，形成一套统一、可靠、完备的大DN值轴承故障诊断新方法，为航空发动机运行安全提供理论和技术支持。

大DN值（内径×转速高达1.0×10^6mm∙r/min）航空发动机轴承故障动态响应由于高速工况、非高斯噪声干扰和非线性复合传递路径调制而呈现出混叠的隐冲击模式，不再表征为明晰的准周期衰减冲击模式，制约了现有轴承诊断方法的有效性，亟需研究大DN值轴承故障诊断新框架，以降低维护成本并避免灾难性事故。本项目基于大DN值轴承隐冲击特征源传播的逆过程，通过搭建多层稀疏学习的架构，开展层级稀疏学习诊断方法研究。.（1）针对非高斯噪声干扰下特征辨识问题，揭示了航空发动机整机振动中各振源信号在不同变换空间的稀疏本征结构，分别提出了结构化稀疏滤波消噪算法和稀疏低秩协同优化算法，建立了不依赖精确噪声模型的非高斯噪声表征机制，实现了航空发动机非高斯噪声的消减；（2）针对故障源信息受到复杂传递路径调制的问题，分析了特征源周期性稀疏的本征结构，分别提出了非负有界稀疏解卷模型、基于Firm阈值算子的稳健稀疏正则、即插即用优化策略驱动的自适应稀疏解卷技术，建立了复杂传递路径的自适应稀疏表征机制，实现了冲击特征源的高精度识别；（3）针对大DN值高速轴承混叠故障特征的辨识问题，揭示了特征信号在低秩空间的多模式耦合病态特性，构建了故障特征在二维空间的聚类低秩正则化约束，提出了聚类低秩正则优化算法，实现了大DN值轴承混叠故障特征的识别。（4）搭建了航空轴承振动测试系统，建立了航空轴承振动数据库，验证了上述提出的模型和算法，形成了一套完备、稳健的航空发动机轴承早期故障辨识新框架，可实现DN值为1.15×10^6mm∙r/min且局部剥落面积为1平方毫米的主轴轴承故障源的精准识别，为保障航空发动机的运行安全提供了科学依据和技术支持。.基于本项目相关的研究成果，发表论文10篇，其中中科院一区SCI论文4篇，中科院二区SCI论文3篇，《西安交通大学学报》论文1篇，EI会议论文2篇，申请发明专利1项。基于本项目的资助与相关研究，项目负责人2021年晋升副教授、长安学者人才支持计划青年学术骨干，获批陕西省自然科学基础研究计划1项、中央高校优秀博士毕业生项目1项、中央高校自然科学高新技术研究培育项目1项；作为核心骨干成员参与国家重点研发计划和国家重大科研仪器研制项目各1项。