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首页> 外文期刊>Mechanical systems and signal processing >Exploiting the inter-correlation of structural vibration signals for data loss recovery: A distributed compressive sensing based approach
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Exploiting the inter-correlation of structural vibration signals for data loss recovery: A distributed compressive sensing based approach

机译：利用结构振动信号进行数据丢失恢复的间相互关联：基于分布式压缩感测的方法

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摘要

Compressive Sensing (CS) is a novel signal sampling technique that can enhance the resiliency of the data transmission process in Structural Health Monitoring (SHM) systems by projecting the raw signal into another domain and transmitting the projected signal instead of the original one. The original signal can later be recovered in the fusion center using the received projected vector and the corresponding projection matrix. In this study, data loss recovery of multiple signals with variable loss ratio is investigated. Unlike previously proposed CS-based single-signal approaches, the inter-correlation across vibration signals is exploited in this study. The inter-correlation type for vibration signals is characterized as having common sparse supports (the same location of non-zero elements in the sparse domain) and is dealt with in the context of Distributed Compressive Sensing (DCS). In this approach, vibration signals are encoded separately without extra inter-sensor communication overhead and are decoded jointly. Adopting the DCS-based approach enabled handling non-uniform data loss pattern across channels by allowing the use of a different projection matrix for each channel. Furthermore, a new projection matrix, named Permuted Random Demodulator (PRD), is proposed that not only reduces the coherence of the sensing matrix and enhances the reconstruction accuracy, but also makes the proposed approach robust to continuous data loss. The performance of the proposed method is evaluated using acceleration responses of a real-life bridge structure under traffic excitation. Modal parameters of the bridge are also identified using the recovered signals with reasonable accuracy.
机译：压缩感测（CS）是一种新的信号采样技术，可以通过将原始信号投影到另一个域并发送投影信号而不是原始的信号来增强结构健康监测（SHM）系统中的数据传输过程的弹性。稍后可以使用接收的投影矢量和相应的投影矩阵在融合中心中恢复原始信号。在该研究中，研究了具有可变损失比的多个信号的数据丢失恢复。与先前提出的基于CS的单信号方法不同，在本研究中利用振动信号的间相互作用。振动信号的相关类型的特征在于具有共同的稀疏支持（在稀疏域中的非零元素的相同位置），并且在分布式压缩感测（DCS）的上下文中被处理。在这种方法中，振动信号在没有额外的传感器间通信开销的情况下单独进行编码，并且共同地解码。采用基于DCS的方法，通过允许对每个信道的不同投影矩阵使用不同的投影矩阵来使能够在通道上处理非均匀数据丢失模式。此外，提出了一种新的投影矩阵，命名允许的随机解调器（PRD），其不仅降低了感测矩阵的相干性并增强了重建精度，而且还使得提出的方法鲁棒到连续数据丢失。通过交通励磁下的实际桥梁结构的加速响应来评估所提出的方法的性能。还使用具有合理精度的恢复信号来识别桥的模态参数。

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