标题： 基于STFT的时间频率模式分解：多分量信号分析的快速稳健方法 显示英文标题

标题： STFT-based Time-Frequency Mode Decomposition: A Fast and Robust Method for Multicomponent Signal Analysis

摘要： 将复杂、多组分和非平稳信号分解为其组成模式是科学和工程中的一个基本但重要的挑战。 现有方法在准确性、计算成本以及对先验信息（如模式数量）的需求之间往往面临权衡。 本文介绍了时频模式分解（TFMD），这是一种用于快速、稳健和自适应分解此类信号的新框架。 TFMD基于这样一个原理：模式在时频域中形成连续的高能量区域。 其非迭代流程将信号分解重新表述为图像分割任务：首先将信号转换为频谱图，然后对其进行平滑处理以增强这些高能量区域的连续性。 随后采用一系列自适应阈值处理和基于大小的连通组件标记来自动分割频谱图并为每个模式生成掩码。 最后通过逆短时傅里叶变换重建模式。 在多种合成信号上的验证表明，TFMD能够准确确定模式数量，并以高保真度进行重建。 其在高噪声条件下的表现尤为突出。 比较分析证实，TFMD在更广泛的信号类型上提供了稳健且具有竞争力的性能，而理论复杂度分析揭示了其由于非迭代设计而具有的优越计算效率。 通过成功地从真实世界的桥梁振动信号中提取模态响应，进一步证明了该方法的实际应用价值。 TFMD为多组分信号分析提供了一种计算效率高且功能强大的范式，在大规模或时间敏感的应用中，提供了准确性、灵活性和效率之间的有力平衡。 显示英文摘要

摘要： The decomposition of complex, multicomponent, and non-stationary signals into their constituent modes is a fundamental yet significant challenge in science and engineering. Existing methods often struggle with a trade-off among accuracy, computational cost, and the need for prior information such as the number of modes. This paper introduces time-frequency mode decomposition (TFMD), a novel framework for the fast, robust, and adaptive decomposition of such signals. TFMD operates on the principle that modes form contiguous high-energy regions in the time-frequency domain. Its non-iterative pipeline reframes signal decomposition as an image segmentation task: a signal is transformed into a spectrogram, which is then smoothed to enhance the continuity of these high-energy regions. A sequence of adaptive thresholding and connected-component labeling with size-based filtering is then employed to automatically segment the spectrogram and generate a mask for each mode. The modes are finally reconstructed via the inverse short-time Fourier transform. Validation on diverse synthetic signals demonstrates that TFMD accurately determines the number of modes and reconstructs them with high fidelity. Its performance is particularly strong in high-noise conditions. A comparative analysis confirms that TFMD provides robust, competitive performance across a wider variety of signal types, while a theoretical complexity analysis reveals its superior computational efficiency stemming from its non-iterative design. The method's practical utility is further demonstrated by successfully extracting modal responses from a real-world footbridge vibration signal. TFMD provides a computationally efficient and powerful paradigm for multicomponent signal analysis, offering a compelling balance of accuracy, versatility, and efficiency for large-scale or time-sensitive applications.