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首页> 外文期刊>Mechanical systems and signal processing >Compressive sensing-based electrostatic sensor array signal processing and exhausted abnormal debris detecting
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Compressive sensing-based electrostatic sensor array signal processing and exhausted abnormal debris detecting

机译：基于压缩感测的静电传感器阵列信号处理及异常碎片检测

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摘要

Highlights•This paper is pioneering in using compressed sensing (CS) to measure the amount and position of abnormal debris.•The measurement model of a hemisphere-shaped electrostatic sensors’ circular array (HSESCA) is discretized into a sparse representation form.•Modeling of the HSESCA, sparse representation, BP-based solution and result calibration have been presented.•The amount of abnormal debris and the number of sensors are important factors affecting the proposed method.AbstractWhen faults happen at gas path components of gas turbines, some sparsely-distributed and charged debris will be generated and released into the exhaust gas. The debris is called abnormal debris. Electrostatic sensors can detect the debris online and further indicate the faults. It is generally considered that, under a specific working condition, a more serious fault generates more and larger debris, and a piece of larger debris carries more charge. Therefore, the amount and charge of the abnormal debris are important indicators of the fault severity. However, because an electrostatic sensor can only detect the superposed effect on the electrostatic field of all the debris, it can hardly identify the amount and position of the debris. Moreover, because signals of electrostatic sensors depend on not only charge but also position of debris, and the position information is difficult to acquire, measuring debris charge accurately using the electrostatic detecting method is still a technical difficulty. To solve these problems, a hemisphere-shaped electrostatic sensors’ circular array (HSESCA) is used, and an array signal processing method based on compressive sensing (CS) is proposed in this paper. To research in a theoretical framework of CS, the measurement model of the HSESCA is discretized into a sparse representation form by meshing. In this way, the amount and charge of the abnormal debris are described as a sparse vector. It is further reconstructed by constrainingl1-norm when solving an underdetermined equation. In addition, a pre-processing method based on singular value decomposition and a result calibration method based on weighted-centroid algorithm are applied to ensure the accuracy of the reconstruction. The proposed method is validated by both numerical simulations and experiments. Reconstruction errors, characteristics of the results and some related factors are discussed.
机译： 突出显示 • 本文率先使用压缩传感（CS）测量异常碎片的数量和位置。 • 测量模型半球形静电传感器的圆形阵列（HSESCA）离散为稀疏表示形式。 • 已经提出了HSESCA的建模，稀疏表示，基于BP的解决方案和结果校准。 < / ce：列表项> • 异常碎片的数量和传感器的数量是影响因素 摘要 当燃气轮机的气路部件发生故障时，会产生一些稀疏分布的带电碎片，并将其释放到废气中。这些碎片称为异常碎片。静电传感器可以在线检测碎屑并进一步指示故障。通常认为，在特定的工作条件下，更严重的故障会产生越来越多的碎屑，而一块较大的碎屑会携带更多的电荷。因此，异常碎片的数量和电荷是故障严重性的重要指标。但是，由于静电传感器只能检测所有碎片的静电场上的叠加效果，因此很难识别碎片的数量和位置。此外，由于静电传感器的信号不仅取决于电荷，还取决于碎片的位置，并且位置信息难以获取，因此，使用静电检测方法准确地测量碎片电荷仍然是技术上的困难。为了解决这些问题，本文采用了半球形静电传感器圆形阵列（HSESCA），并提出了一种基于压缩传感（CS）的阵列信号处理方法。为了在CS的理论框架中进行研究，通过网格划分将HSESCA的测量模型离散化为稀疏表示形式。这样，异常碎片的数量和电荷被描述为稀疏向量。在求解欠定方程时，可以通过约束 l 1 -范数来进一步重构。另外，基于奇异值分解的预处理方法和基于加权质心算法的结果标定方法被应用于保证重构的准确性。通过数值模拟和实验验证了该方法的有效性。讨论了重建错误，结果的特点以及一些相关因素。

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  • 来源
    《Mechanical systems and signal processing》 |2018年第may15期|404-426|共23页
  • 作者

    Xin Tang; Zhongsheng Chen; Yue Li; Yongmin Yang;

  • 作者单位

    Science and Technology on Integrated Logistics Support Laboratory, National University of Defense Technology;

    Science and Technology on Integrated Logistics Support Laboratory, National University of Defense Technology,College of Electrical & Information Engineering, Hunan University of Technology;

    Science and Technology on Integrated Logistics Support Laboratory, National University of Defense Technology;

    Science and Technology on Integrated Logistics Support Laboratory, National University of Defense Technology;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Electrostatic sensor array; Compressive sensing; Abnormal debris; Sparsity;

    机译：静电传感器阵列;压感;异常碎片;稀疏;

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