## 1 Introduction
In recent years, with advancements in vibration diagnostic technologies, Operational Transfer Path Analysis (OTPA) has garnered significant attention for its unique advantage of identifying and quantifying the contribution of each excitation source to the vibration at a target point under actual operating conditions [34; 35; 36]. This method, which does not require system disassembly, can effectively handle Multiple-Input Multiple-Output (MIMO) vibration problems. It has been successfully applied in fields such as the automotive, rail transit, and construction machinery industries, for instance, in identifying vehicle interior noise sources and analyzing the vibro-acoustic transfer paths of train bogies [37; 38; 39; 40]. [PERSON] et al. [41] assessed a variety of measurement-based methods for separating railway noise, showcasing OTPA's place among a suite of diagnostic tools. However, a well-documented challenge in OTPA is the potential for an ill-conditioned input matrix, especially under complex operational conditions where excitation sources are highly correlated or measurement noise is present. This ill-conditioning can compromise the accuracy and stability of the estimated transfer functions. To address this, various improvementshave been proposed. For example, [PERSON] et al. [42] suggested a two-step approach using artificial hammer excitation to generate linearly independent responses, thereby improving the quality of the transmissibility matrix. To address the issue of limited computational accuracy in OTPA caused by the ill-conditioning of the input matrix under complex conditions, researchers have introduced improvements to the algorithm. Methodological advancements, such as the development of Decoupled Transfer Path Analysis (DTPA), have also sought to overcome the challenge of path crosstalk in complex systems [43]. In addition, the integration of Singular Value Decomposition (SVD) for data dimensionality reduction and denoising has enhanced the method's robustness and accuracy in practical applications [44]. Despite these advancements, the systematic application of an improved OTPA framework to diagnose the multi-source vibration characteristics of a combine harvester chassis remains relatively insufficient. While some studies have applied OTPA to harvesters [45], a detailed, robust analysis that overcomes the inherent ill-conditioning problem to clearly quantify source contributions and identify critical transfer paths is still needed. This study aims to fill this gap by proposing and validating a robust diagnostic framework, which employs an improved OTPA method with SVD-based proportional truncation, to decouple the complex, multi-source vibrations in a combine harvester chassis. Given the significant structural complexity of the harvester chassis, which makes the development of an accurate analytical mass-stiffness-damping model exceptionally challenging, the present study focuses on the chassis frame of a crawler-type combine harvester, aiming to comprehensively analyze its vibration response characteristics under multi-source excitation through a combination of theoretical analysis, experimental validation, and an improved OTPA method. Specifically, this research will first reveal the fundamental mechanical characteristics of the chassis frame and identify the excitation features of its main working components. Subsequently, the improved OTPA method will be employed as a key tool to quantify the vibration contributions from primary excitation sources--such as the engine, cutting header, threshing cylinder, and vibrating screen--to critical target points on the chassis frame. Ultimately, the study will identify the key paths through which vibration energy is transmitted from the main excitation sources to the chassis frame. The findings of this research are intended to provide a crucial theoretical basis and data support for the precise vibration reduction and optimization design of the combine harvester chassis frame.