Design of a Conveyer Trough Bolt Signal Acquisition System and Bayesian Ensemble Identification Method for Working State

Yi Lian, Bangzhui Wang, Meiyan Sun, Kexin Que, Sijia Xu, Zhong Tang () and Zhilong Huang
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Yi Lian: School of Transportation Engineering, Jiangsu Shipping College, Nantong 226010, China
Bangzhui Wang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Meiyan Sun: School of Transportation Engineering, Jiangsu Shipping College, Nantong 226010, China
Kexin Que: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Sijia Xu: School of Energy and Power Engineering, Jiangsu University, Zhenjiang 212013, China
Zhong Tang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Zhilong Huang: School of Transportation Engineering, Jiangsu Shipping College, Nantong 226010, China

Agriculture, 2025, vol. 15, issue 9, 1-29

Abstract: Rice combine harvester conveyor troughs and their bolted connections are susceptible to vibration-induced failure due to operational and environmental excitations. Addressing the challenge of predicting the state of the combine harvester’s conveyor trough bolted structure prior to vibration-induced failure, this study addresses this by investigating signal analysis, system design, and condition identification for these critical components. Firstly, multi-point vibration signals from the conveyor trough were acquired and analyzed in the time-frequency domain. The analysis pinpointed the X-direction at the trough-frame connection (Point 5) as the most responsive location, with RMS peaking at 6.650 during header start-up (vs. 0.849 idle). Significant responses were also noted at Point 3 (Y-dir, 4.628) and Point 6 (X-dir, 3.896) under certain conditions (where Z-direction responses were minimal), identifying critical points that form the basis for condition assessment. Secondly, a vibration acquisition system was developed using a high-performance AD7606 ADC and A39C wireless technology. It features 16-bit resolution (0.00076 mm/s theoretical sensitivity), 8-channel synchronous sampling up to 200 kSPS, and rapid (0.8 s) wireless data transmission. This system meets the demands for high-frequency, high-precision monitoring of the bolted structure. Finally, after comparing machine learning algorithms, Support Vector Machine was chosen for its superior performance. Using a one-vs.-one strategy and data from critical points, an operational condition identification model was developed. Validation with field data confirmed high accuracy (96.9–99.7%) for principal states and low misclassification rates (<5%). This allows for precise identification of the bolted structure’s working status. The research presented in this study offers effective methodologies and technical underpinning for the condition monitoring of critical structural components in rice combine harvesters.

Keywords: signal analysis; signal acquisition; monitoring system; combine harvester; feature recognition (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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