Development and Test of Frequency Subsection Regulation System for Combine Harvester Header Cutter

Liu, Ying; Guan, Zhuohuai; Chen, Yisong

doi:10.35633/inmateh-65-30

Cited by 11 publications

(3 citation statements)

References 12 publications

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“…Establishing a confusion matrix and verifying the performance of corn grain harvester failure prediction by confusion matrix (Wang et al, 2021).…”

Section: Simulation Experimentsmentioning

confidence: 99%

Fault Prediction Model of Corn Grain Harvester Based on Selfcoding Neural Network

WANG,

ZHANG,

YAO

et al. 2023

INMATEH

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The corn grain harvester serves as an example of complex farming machinery with a condition monitoring system that collects a lot of working condition data, making it challenging to identify the true change pattern due to the data coming from the equipment in various states. Firstly, the overall structure of the corn grain harvester is analyzed, and the common causes and mechanisms of corn grain harvester failures are analyzed, leading to the cutting table as the main research object; Secondly, by collecting historical failure data of corn grain harvester as well as real-time failure information for collation and pre-processing, eliminating interference such as noise and missing data, establishing a failure matrix, extracting internal characteristics between failure causes and establishing a mapping between failure causes and failure phenomena; Finally, the future failure phenomena of the corn grain harvester are predicted according to different failure causes. The simulation analysis results show that the self-coding neural network fault prediction model can better predict the occurrence probability and types of faults and provide data support for fault maintenance and decision making of agricultural machinery.

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“…Establishing a confusion matrix and verifying the performance of corn grain harvester failure prediction by confusion matrix (Wang et al, 2021).…”

Section: Simulation Experimentsmentioning

confidence: 99%

Fault Prediction Model of Corn Grain Harvester Based on Selfcoding Neural Network

WANG,

ZHANG,

YAO

et al. 2023

INMATEH

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“…The soft and easily damaged characteristics of cabbage make their mechanized harvesting quality fluctuate during actual production work. The conveying mechanism structure of cabbage is mostly chain clamping [4], screw conveying [5], and clamping conveying [6]. The CKM-1 [7] and NKH-1 [8] single-row cabbage harvesters developed by the USSR adopt chain clamping and conveying, which have low harvesting efficiency and great damage.…”

Section: Introductionmentioning

confidence: 99%

Design and Experiment of a Low-Loss Harvesting Test Platform for Cabbage

et al. 2023

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In order to explore the mechanism and influence mechanism of cabbage harvest damage, a low-loss cabbage harvest test platform was designed on the basis of combining the physical characteristics of cabbage with the mechanical characteristics of mechanical harvest and the cabbage harvest operation process. Through the design of key components of the test platform harvesting, the key parameters of the pulling-out device, the reel device, the flexible clamping and conveying device, and the double-disc cutting device were determined. The movement changes of cabbage during pulling out, conveying, and cutting were analyzed to clarify the process of damage generation and critical conditions of damage in cabbage harvesting operations. The test results showed that when the speed of the pulling out device was controlled at 80–120 r/min, the speed of the clamping and conveying device was controlled at 120–240 r/min, and the speed of the double disc cutter was controlled at 140–180 r/min, the average success rate of pulling on the low-loss harvesting test platform was 92.7%; the average damage rate of the pulling process was 7.32%; the average success rate of clamping and conveying was 88.6%; the average damage rate of the clamping and conveying link was 12%; the average success rate of root cutting was 89.3%; and the average damage rate of the cutting link was 11.34%. The average qualified rate of harvesting in the pulling link was 86.7%, the average qualified rate of harvesting in the clamping and conveying link was 75.3%, and the average qualified rate of harvesting in the cutting link was 77.3%. All the performance indicators meet the design requirements and relevant standards, and the research results can provide a reference for the development and structural improvement of low-loss harvesting equipment for cabbage.

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“…Each node and mechanism does its own specific job. Contrary to this, one of the defining features of the machine's efficiency is its reliability [3].…”

Section: Introductionmentioning

confidence: 99%

Failure analysis of the segment finger bar mower and force interaction of the blade segment with the plant stem

Banniy¹,

Popyk²,

Savko³

2022

Tehnìka ta energetika

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Scientific observations on the quality and reliability of combine harvesters have shown that of all the delays of equipment, 32...35% of the total operating time, technical failures account for 17.4...19.8%. Most of the failures were observed with the failure of combine parts, mechanical transmissions, hydraulic systems, etc. Quantitative analysis of harvester failures has shown that most failures occur in the operation of the cutting device, namely the finger bar mower. The working hypothesis is that it is possible to increase the reliability of the cutting device of harvesting machines in ordinary operating conditions and in the event of sudden failures by applying a coating on the blade segment, without significantly changing its geometric parameters, and performing notches that ensure both a decrease in the mass of the segment and its destruction without plastic deformation when foreign objects enter the cutting zone. The main problem of the reliability of segments of cutting device, harvesting equipment, is the cutting edge of the blade, which wears out and deforms during operation. From the above, it follows that the purpose of the study is to improve the reliability of segment finger bar mower machines by improving the mechanical properties of segments, these investigations are relevant and of practical interest. The study includes the use of mathematical probability and reliability theory, analysis, and a systematic approach to provide an analytical description of the operation of a cutting device segment. The consequence of this scientific investigation is an analysis of failures and malfunctions of the machinery, presented graphically, a diagram of how a plant stem segment is cut and the forces acting on the stem and the segment during the cutting process are depicted. These materials have scientific and informative content that would be of interest to engineers and scientists in the process of research and production of cutting device segments

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Development and Test of Frequency Subsection Regulation System for Combine Harvester Header Cutter

Cited by 11 publications

References 12 publications

Fault Prediction Model of Corn Grain Harvester Based on Selfcoding Neural Network

Fault Prediction Model of Corn Grain Harvester Based on Selfcoding Neural Network

Design and Experiment of a Low-Loss Harvesting Test Platform for Cabbage

Failure analysis of the segment finger bar mower and force interaction of the blade segment with the plant stem

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