## 5 Comprehensive Discussion  
The application of non-parametric dependent signal processing, adaptive signal processing, and deconvolution techniques in the mechanical field exhibits unique characteristics and entails significant advantages as well as inherent limitations. Non-parametric signal processing methods, including short-time Fourier transform (STFT) and Wigner-Ville distribution (WVD), have found widespread usage in mechanical fault diagnosis due to their simplicity and intuitive properties. These techniques do not necessitate prior knowledge of the signal's statistical properties, enabling direct analysis in the time-frequency domain and effective extraction of fault characteristics. However, in complex and variable mechanical signal environments, non-parametric methods may face limitations in analysis accuracy due to insufficient prior knowledge, making it challenging to accurately capture subtle, dynamic changes within the signal. Adaptive signal processing technology achieves a high degree of adaptability to changes in signal environments by incorporating optimal criteria, such as adaptive filtering and adaptive noise cancellation techniques. These technologies can track signal characteristics in real-time, effectively addressing non-stationarity and uncertainty factors in mechanical systems. Nonetheless, when dealing with highly nonlinear mechanical signals, adaptive signal processing may encounter issues related to slow convergence speeds, particularly when signal characteristics undergo rapid changes. The algorithm may struggle to adapt promptly to the new signal environment, potentially impacting the accuracy and real-time performance of diagnosis. As a signal recovery technique, deconvolution technology has demonstrated notable advantages in fields like mechanical image processing and vibration signal analysis. By performing deconvolution operations, the blurring effects and convolution components within the signal can be effectively removed, restoring the characteristic information of the original signal. This is particularly crucial for extracting weak fault characteristics in mechanical systems. However, in practical applications, deconvolution techniques may be significantly impacted by noise and interference. Mechanical signals are frequently contaminated with substantial background noise and interference components, which can be amplified or introduce new error sources during the deconvolution process, thereby reducing the accuracy and reliability of signal recovery. Given these considerations, in practical applications, it is imperative to meticulously select appropriate signal processing methods based on specific requirements or adopt a strategy that integrates multiple methods for processing. Taking mechanical fault diagnosis as an illustrative example, non-parametric dependent signal processing methods can be initially employed to conduct a preliminary time-frequency domain analysis of the signal, thereby extracting potential fault characteristics. Subsequently, adaptive signal processing technology can be utilized to further filter noise interference and enhance signal quality. Finally, deconvolution technology can be applied to recover clearer and more precise fault feature information. This strategy of integrating multiple signal processing techniques can fully exploit their respective technical advantages, significantly improving the accuracy and efficiency of mechanical fault diagnosis. Table 1 summarizes the application scenarios and optimization methods of the key signal decomposition techniques highlighted in this review. Table 2 Comparison of the signal processing techniques for structural health monitoring (SHM)