## 1 Introduction  
The performance and reliability of a combine harvester, which is an essential piece of agricultural equipment in modern agricultural production, directly impacts the efficiency and quality of staple food production. It is of significant importance to timely detect faults in the combine harvester to improve its reliability. Typically, fault diagnosis relies on vibration signals [1]. However, the complex structure of the combine harvester results in a complex coupling relationship between vibration signals and serious mutual interference. Moreover, the collected vibration signals exhibit non-smooth and non-periodic characteristics [2]. Lastly, due to the large structural shape of the combine harvester, the installation position of the sensors is far from the location where issues occur, leading to more interference signals. Therefore, it is necessary to perform noise reduction to obtain more accurate signals. Presently, researchers in the field of vibration signal processing have proposed several efficient algorithms for reducing noise. These methods include the short-time Fourier transform, wavelet transform (WT, CWT), and adaptive decomposition techniques such as empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) [3; 4; 5]. The EMD technique was introduced by [PERSON] through the Hilbert-Huang Transform (HHT) and has been widely used. However, EMD itself has certain limitations, including mode aliasing, endpoint effects, and constraints on stopping conditions [6; 7]. To address the issue of mode aliasing, a noise-assisted signal decomposition technique called EEMD has been developed [8; 9]. This method improves upon EMD by incorporating Gaussian white noise into the original signal, effectively suppressing the confounding effects associated with mode aliasing [10]. Nevertheless, this augmentation introduces the challenge of residual noise. To address the issue of residual noise, [PERSON] et al. [11] proposed a supplementary method called CEEMD. By introducing positive and negative Gaussian white noise and subsequently performing ensemble averaging on the decomposition results, the noise can be effectively attenuated, thus reducing the residual noise. Both the EEMD and CEEMD algorithms inevitably generate a certain amount of residual white noise in the obtained IMFs. This residual noise adversely affects the subsequent analysis and processing of the signal. Additionally, maintaining consistency in the number of decomposed IMF components is challenging. As a result, the completeness of the decomposition cannot be guaranteed during the ensemble averaging process. To overcome this problem, [PERSON] et al. [12] proposed CEEMDAN, and it has since seen widespread application. However, CEEMDAN also has some problems, such as the presence of some residual noise in the decomposed IMF; and the presence of some spurious components in the results of signal decomposition [13]. To make the signal have a better decomposition effect, an improved CEEMDAN method is proposed, which is based on CEEMDAN decomposition, and the improved complete ensemble empirical mode decomposition with adaptive noise [14; 15] can effectively solve the phenomenon of modal aliasing and improve the inherent modal function obtained by the decomposition of the existence of the noise residuals and the problem of the spurious component. The improved CEEMDAN algorithm can effectively decompose the signal, reduce the spurious components and improve the decomposition accuracy. However, the IMF decomposed by this method still contains a certain amount of noise. To further improve the decomposition quality of the vibration signals from a combined harvester and facilitate the extraction of local features, the singular value decomposition (SVD) method was employed to reduce the noise in the IMF signals of each order obtained after the improved CEEMDAN decomposition. This aims to accurately extract the local features of the vibration signals.