Structural Damage Detection Using Mutual Information and Improved Reptile Search Algorithm for Fused Smooth Signals Affected by Coloured Noise

IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Sahar Hassani
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Abstract

Structural health monitoring (SHM) faces a significant challenge in accurately detecting damage due to noise in acquired signals in composite plates, which can adversely affect reliability. Specific noise reduction techniques tailored to SHM signals are developed to tackle this issue. Gaussian smoothing proves effective in reducing noise and enhancing signal features, thereby facilitating the identification of damage-related information. Optimization algorithms play a crucial role in damage detection, especially when integrated with smoothing and fusion techniques, as they provide optimal solutions to SHM challenges. A model-updating-based optimization algorithm is proposed for detecting damage in structures using condensed frequency response functions (CFRFs), even in the presence of various types of noise and measurement errors. The CFRF signals are first smoothed using an optimized Gaussian smoothing technique as part of the proposed method. Then, the proposed methodology integrates diverse smoothed signals using a raw data fusion approach, including those from different excitations, frequency ranges, and sensor placements. Fused smoothed signals are then fed into a new objective function, incorporating mutual information (MI) and Gaussian smoothing to mitigate correlated coloured noise. The proposed objective function also introduces a hyperparameter tuning of Gaussian smoothing to enhance its performance. Optimization via the improved reptile search algorithm (IRSA) updates the objective function, optimizing damage and smoothing parameters. The hybrid method detects damage in numerical composite laminated plates with different layers and boundary conditions, demonstrating its effectiveness as an SHM technique. Comparative evaluations of other state-of-the-art methods show that the proposed method outperforms its counterparts, making it a promising damage detection approach to address the noise challenge in the SHM field.

Abstract Image

针对受彩色噪声影响的融合平滑信号,使用互信息和改进的爬行搜索算法进行结构损伤检测
结构健康监测(SHM)在准确检测复合板损伤方面面临着巨大挑战,因为采集信号中的噪声会对可靠性产生不利影响。为解决这一问题,开发了专门针对 SHM 信号的降噪技术。事实证明,高斯平滑技术可有效降低噪声并增强信号特征,从而促进损伤相关信息的识别。优化算法在损伤检测中发挥着至关重要的作用,尤其是与平滑和融合技术相结合时,因为它们能为 SHM 面临的挑战提供最佳解决方案。本文提出了一种基于模型更新的优化算法,即使在存在各种噪声和测量误差的情况下,也能利用压缩频率响应函数(CFRF)检测结构中的损伤。作为建议方法的一部分,首先使用优化的高斯平滑技术对 CFRF 信号进行平滑处理。然后,建议的方法使用原始数据融合方法整合各种平滑信号,包括来自不同激励、频率范围和传感器位置的信号。然后,将融合后的平滑信号输入一个新的目标函数,其中包含互信息(MI)和高斯平滑,以减轻相关的彩色噪声。拟议的目标函数还引入了高斯平滑的超参数调整,以提高其性能。通过改进爬行动物搜索算法(IRSA)进行优化,更新目标函数,优化损伤和平滑参数。该混合方法可检测具有不同层和边界条件的数值复合层压板的损伤,证明了其作为 SHM 技术的有效性。与其他最先进方法的比较评估表明,所提出的方法优于同类方法,使其成为应对 SHM 领域噪声挑战的一种有前途的损伤检测方法。
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来源期刊
Structural Control & Health Monitoring
Structural Control & Health Monitoring 工程技术-工程:土木
CiteScore
9.50
自引率
13.00%
发文量
234
审稿时长
8 months
期刊介绍: The Journal Structural Control and Health Monitoring encompasses all theoretical and technological aspects of structural control, structural health monitoring theory and smart materials and structures. The journal focuses on aerospace, civil, infrastructure and mechanical engineering applications. Original contributions based on analytical, computational and experimental methods are solicited in three main areas: monitoring, control, and smart materials and structures, covering subjects such as system identification, health monitoring, health diagnostics, multi-functional materials, signal processing, sensor technology, passive, active and semi active control schemes and implementations, shape memory alloys, piezoelectrics and mechatronics. Also of interest are actuator design, dynamic systems, dynamic stability, artificial intelligence tools, data acquisition, wireless communications, measurements, MEMS/NEMS sensors for local damage detection, optical fibre sensors for health monitoring, remote control of monitoring systems, sensor-logger combinations for mobile applications, corrosion sensors, scour indicators and experimental techniques.
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