Research on Load Spectrum Reconstruction Method of Exhaust System Mounting Bracket of a Hybrid Tractor Based on MOPSO-Wavelet Decomposition Technique

Liming Sun, Mengnan Liu, Zhipeng Wang, Chuqiao Wang and Fuqiang Luo ()
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Liming Sun: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Mengnan Liu: State Key Laboratory of Intelligent Agricultural Power Equipment, Luoyang 471039, China
Zhipeng Wang: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Chuqiao Wang: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Fuqiang Luo: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2023, vol. 13, issue 10, 1-18

Abstract: To overcome the limitations of the hybrid tractor bumping tests, which include extended cycle times, high costs, and impracticality for single-part reliability verification, this study focuses on the exhaust system mounting bracket of a hybrid tractor. A novel approach that combines multi-objective particle swarm optimization (MOPSO) and wavelet decomposition algorithms was employed to enhance the reconstruction of shock vibration signals. This approach aims to enable the efficient acquisition of input signals for subsequent shaker table testing. The methodology involves a systematic evaluation of the spectral correlation between the original signal and the reconstructed signal at the stent’s response position, along with signal compression time. These parameters collectively constitute the objective function. The multi-objective particle swarm optimization algorithm is then deployed to explore a range of crucial parameters, including wavelet basic functions, the number of wavelet decomposition layers, and the selection of wavelet components. This exhaustive exploration identifies an optimized signal reconstruction method that accurately represents shock vibration loads. Upon rigorous screening based on our defined objectives, the optimal solution vector was determined, which includes the utilization of the dB10 wavelet basic function, employing a 12-layer wavelet decomposition, and selecting wavelet components a12 and d3~d11. This specific configuration enables the retention of 95% of the damage coefficients while significantly compressing the test time to just 46% of the original signal duration. The implications of our findings are substantial as the reconstructed signal obtained through our optimized approach can be readily applied to shaker excitation. This innovation results in a notable reduction in test cycle time and associated costs, making it particularly valuable for engineering applications, especially in tractor design and testing.

Keywords: hybrid tractor; multi-objective particle swarm optimization; wavelet decomposition; durability; shock response spectrum (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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