Assembly Quality Inspection of Combine Harvester Based on Whale Algorithm Optimization LSSVM

In order to detect the assembly quality of the combine harvester accurately and effectively, a method for the assembly quality inspection of the combine harvester based on the improved whale algorithm (IWOA) to optimize the least square support vector machine is proposed. Aiming at the characteristi...

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Bibliographic Details
Published in:Shock and vibration 2022-01, Vol.2022, p.1-12
Main Authors: Zhao, Sixia, Ma, Yizhen, Liu, Mengnan, Chen, Xiaoliang, Xu, Liyou
Format: Article
Language:English
Subjects:
Accuracy
Adaptive control
Algorithms
Analysis
Assembly
Combine harvesters
Fault diagnosis
Foraging behavior
Inspection
Marine mammals
Mathematical optimization
Optimization
Optimization algorithms
Population
Quality control equipment
Support vector machines
Trigonometric functions
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Summary:In order to detect the assembly quality of the combine harvester accurately and effectively, a method for the assembly quality inspection of the combine harvester based on the improved whale algorithm (IWOA) to optimize the least square support vector machine is proposed. Aiming at the characteristics of whale optimization algorithm’s weak search ability and easy maturity, this paper introduces the cosine control factor and the sine time-varying adaptive weight to improve it and uses the benchmark function to verify the general adaptability of the algorithm. Combined with the local mean decomposition (LMD), the assembly quality inspection model of the combine harvester was established and applied to the Dongfanghong 4LZ-9A2 combine harvester for experimental verification. The experimental results show that the IWOA proposed in this paper has better optimization ability and adaptability. The average accuracy of the IWOA model proposed in this paper reaches 90.5%, which is 4% higher than that of the WOA model, and the standard deviation of the average accuracy is reduced by 0.15%, which indicates that the IWOA model has better stability.
ISSN:1070-9622
1875-9203
DOI:10.1155/2022/5181360