Vibration in passenger cabins of turboprop airplanes is a serious challenge. One of the essential steps in studying the cabin vibrations is to determine the contributing sources of vibration. The vibration signals are highly nonstationary and noisy. Therefore, one may require a noise-tolerant signal processing method for decomposition of the signals. In this article, the wavelet-based empirical mode decomposition is introduced for the first time to improve the performance of the traditional empirical mode decomposition in dealing with noise. Unlike the traditional empirical mode decomposition that extracts the signal trend by averaging the upper and lower envelopes intersecting local maxima and minima of the signal, the wavelet-based empirical mode decomposition directly extracts the signal trend by applying the multilevel wavelet decomposition of the consecutive approximations within the sifting process. Numerical studies are undertaken to evaluate the effect of noise on the performance of the empirical mode decomposition and wavelet-based empirical mode decomposition. Also, comparisons are made between the methods at dissimilar noise powers based on the orthogonality, integral, and energy decomposition criteria. The results indicate that both methods generate similar results in the absence of noise. Considering the number of obtained intrinsic mode functions, decomposition quality criteria, and computational cost, however, the wavelet-based empirical mode decomposition outperforms the classic method at higher noise levels. In this article, the wavelet-based empirical mode decomposition is used for analysis of in-flight airplane cabin vibration. A 52-passenger turboprop aircraft is equipped with eight triaxial piezoelectric accelerometers, and several flight tests are performed to acquire in-flight vibration signals within the passenger cabin. The proposed wavelet-based empirical mode decomposition is applied to the experimental data. Then, the amplitudes and frequencies of the intrinsic mode functions are examined. Finally, the probable vibration sources are identified based on the intrinsic mode functions characteristics.

涡轮螺旋桨飞机客舱的振动是一个严峻的挑战。研究机舱振动的基本步骤之一是确定振动的来源。振动信号非常不稳定且有噪声。因此，可能需要一种耐噪声的信号处理方法来分解信号。本文首次引入基于小波的经验模态分解，以提高传统经验模态分解在处理噪声方面的性能。与传统的经验模式分解不同，传统的经验模式分解通过平均与信号的局部最大值和最小值相交的上下包络来提取信号趋势，基于小波的经验模式分解通过在筛选过程中应用连续逼近的多级小波分解直接提取信号趋势。进行了数值研究，以评估噪声对经验模态分解和基于小波的经验模态分解性能的影响。此外，还基于正交性，积分和能量分解标准对不同噪声功率下的方法进行了比较。结果表明，两种方法在没有噪声的情况下都能产生相似的结果。考虑到获得的固有模式函数的数量，分解质量标准和计算成本，基于小波的经验模式分解在较高噪声水平下的性能优于经典方法。在本文中，基于小波的经验模态分解被用于飞行中飞机机舱振动的分析。一架可容纳52人的涡轮螺旋桨飞机配备了八个三轴压电加速度计，并进行了几次飞行测试，以获取客舱内的飞行中振动信号。提出的基于小波的经验模态分解被应用于实验数据。然后，检查固有模式函数的幅度和频率。最后，根据固有模式函数特征确定可能的振动源。并进行了几次飞行测试，以获取乘客舱内的飞行中振动信号。提出的基于小波的经验模态分解被应用于实验数据。然后，检查固有模式函数的幅度和频率。最后，根据固有模式函数特征确定可能的振动源。并进行了几次飞行测试，以获取乘客舱内的飞行中振动信号。提出的基于小波的经验模态分解被应用于实验数据。然后，检查固有模式函数的幅度和频率。最后，根据固有模式函数特征确定可能的振动源。