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Collaborative sparse classification for aero-engine's gear hub crack diagnosis

机译：航空发动机齿轮毂裂纹诊断的协作稀疏分类

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摘要

It is a big challenge to robustly detect the early crack fault of the differential gear train's gear-hub of an aero-engine from the vibration signals of its engine casing, because of imprecise dynamic model guidance, extremely weak signature, complex modulation effects and limited training data. In this paper, a novel collaborative sparse classification framework (CSC), which collaborates the prior knowledge based sparse filtering and data-driven classification strategy, is proposed as a new endeavor for health condition assessment of aero-engine's gear-hub. The sparse filtering model collaborates the empirically established fault pattern and its intrinsic local self-similar properties, by which the feature to interference ratio is enhanced. Subsequently, a sparse classification method is adopted to further explore the latent discriminative signatures and thus the health conditions of gear-hub can be automatically recognized. This work can not only recognize the abnormal vibration with high accuracy but also locate is source component to some extent. The effectiveness, superiority, parameter robustness and generalization performance of the proposed framework are thoroughly demonstrated by enormous comparison experiments with the state-of-the-arts.
机译：由于其不精确的动态模型指导，极弱的特征，复杂的调制效果和有限的功能，从其发动机壳体的振动信号中可靠地检测出航空发动机差动齿轮系齿轮轮毂的早期裂纹故障是一项巨大的挑战。训练数据。在本文中，提出了一种新颖的协作式稀疏分类框架（CSC），它将基于先验知识的稀疏过滤和数据驱动的分类策略进行了协作，作为航空发动机轮毂健康状况评估的一项新尝试。稀疏滤波模型结合经验建立的故障模式及其固有的局部自相似特性，从而增强了特征干扰比。随后，采用稀疏分类方法来进一步探索潜在的识别特征，从而可以自动识别齿轮轮毂的健康状况。这项工作不仅可以高精度地识别异常振动，而且可以在某种程度上定位源组件。通过与最新技术的大量比较实验，充分证明了所提出框架的有效性，优越性，参数鲁棒性和泛化性能。

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  • 来源
    《Mechanical systems and signal processing》 |2020年第7期|106426.1-106426.24|共24页
  • 作者

    Han Zhang; Xuefeng Chen; Wei Chen; Zhixian Shen;

  • 作者单位

    Key Laboratory of Road Construction Technology and Equipment of MOE Chang'an University Xi'an 710064 China;

    State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Xi'an 710049 China Collaborative Innovation Center of High-End Manufacturing Equipment Xi'an Jiaotong University Xi'an 710054 China;

    Air Force Engineering University Xi'an 710051 China;

    Collaborative Innovation Center of High-End Manufacturing Equipment Xi'an Jiaotong University Xi'an 710054 China Air Force Engineering University Xi'an 710051 China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Aero-engine; Gear hub crack; Collaborative sparse filtering; Sparse classification; Fault diagnosis;

    机译：航空发动机齿轮毂裂纹;协同稀疏过滤;稀疏分类;故障诊断;

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