Failure State Identification and Fault Diagnosis Method of Vibrating Screen Bolt Under Multiple Excitation of Combine Harvester

Jiaojiao Xu (), Tiantian Jing, Meng Fang, Pengcheng Li and Zhong Tang
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Jiaojiao Xu: Higher Vocational Technical College, Shanghai University of Engineering Science, Shanghai 200437, China
Tiantian Jing: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Meng Fang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Pengcheng Li: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Zhong Tang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2025, vol. 15, issue 5, 1-22

Abstract: The demanding operational conditions of combine harvesters induce substantial vibrations and component degradation, significantly impacting harvesting efficiency, safety, and overall machine reliability. Bolt loosening, a critical failure mode at the joints of various working parts of combine harvesters, is a prevalent concern. The complexity and heterogeneity of vibration signals in these machines present a considerable challenge for the timely and accurate detection of bolt loosening. This paper proposes a novel methodology for identifying and diagnosing vibrating screen bolt failure states under multiple excitation conditions, specifically tailored for the 4LZY-1.8(PRO688Q) combine harvester. The study initially analyzes the critical torque associated with bolt connection failure. Subsequently, vibration signals are acquired from the bolt connection of the vibrating screen, and time-frequency analysis is performed to characterize the degree of bolt loosening, the predominant vibration direction, and the causative frequency components. A high-dimensional feature matrix is then constructed utilizing a Gaussian kernel function. The efficacy of the proposed methodology is evaluated through training and testing a classification decision model. This study provides a robust theoretical foundation for the vibration-based fault diagnosis of bolt structures in combine harvesters.

Keywords: combine harvester; bolt failure state recognition; time-frequency characteristics; support vector machine; multi-fusion matrix (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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