In this paper, a hybrid damage detection technique involving a combination of variational mode decomposition (VMD) and frequency domain decomposition (FDD) has been applied to study the effectiveness of damage detection in presence of heavily noise contaminated environment. Damage of small magnitude has been tested under Gaussian pulse noise ranging from 0 to 100% for damage analysis. FDD, a signal processing algorithm, has system identification capability in the presence of noise but requires the output acceleration data from all the sensors installed in the nodes of a structure to identify damage. To reduce the number of sensor data needed to identify damage, wavelet-based algorithms have been used to obtain intrinsic mode functions (IMFs) from single sensor output. These IMFs are then fed to FDD algorithm to obtain the natural frequencies of the structure. For comparison purpose, the algorithms (empirical mode decomposition (EMD) + FDD, and VMD + FDD) have been applied to ASCE benchmark building, which has been set as a common platform, using sensor data of first storey. It was observed that the VMD + FDD gives satisfactory damage identification results for 100% noise contamination whereas EMD + FDD was unable to identify damage accurately for noise above 20%. The robustness of VMD + FDD has been established for a different type of noise, random-valued impulse noise, applied on the benchmark structure for detecting the structural parameters. The hybrid algorithm was also checked for system identification using the sensor data of fourth storey to establish its robustness against the sensitivity of the sensor location.

中文翻译：

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重噪声条件下混合分解算法在结构健康监测中的性能

在本文中，结合了变分模式分解（VMD）和频域分解（FDD）的混合损伤检测技术已被用于研究在严重噪声污染的环境中损伤检测的有效性。小量损伤已在0至100％的高斯脉冲噪声下进行了测试，以进行损伤分析。FDD是一种信号处理算法，在存在噪声的情况下具有系统识别功能，但需要安装在结构节点中的所有传感器的输出加速度数据来识别损坏。为了减少识别损坏所需的传感器数据的数量，基于小波的算法已用于从单个传感器输出中获取固有模式函数（IMF）。然后将这些IMF馈入FDD算法以获得结构的固有频率。为了进行比较，已将算法（经验模式分解（EMD）+ FDD和VMD + FDD）应用于使用第一层传感器数据设置为通用平台的ASCE基准测试。可以看出，对于100％的噪声污染，VMD + FDD给出了令人满意的损伤识别结果，而对于20％以上的噪声，EMD + FDD无法准确识别出损伤。已针对不同类型的噪声（随机值脉冲噪声）建立了VMD + FDD的鲁棒性，将其应用于基准结构以检测结构参数。