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The six-component forces at the wheel-road interaction represent the sole coupling between the vehicle and the road surface, and obtaining these forces is critical for conducting reliability and durability assessments of the entire vehicle. In response to the high cost, long cycle, and low efficiency associated with traditional methods for obtaining wheel six-component forces, a data-driven approach for rapidly predicting wheel loads was proposed. Firstly, for the non-stationary random signals on real vehicle roads, a joint method of the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), permutation entropy (PE), and wavelet threshold denoising (WTD) was applied for the data denoising.Secondly, the easily obtainable and low-cost data, such as wheel center acceleration, damper displacement, and center of mass acceleration, were used as inputs. Various neural network architectures with nonlinear transfer relationships were designed for multi-surface wheel six-component force prediction. A multi-dimensional load prediction evaluation system was established in the time domain, frequency domain, and damage domain. Finally, in order to overcome the challenges of a large and costly training dataset, an input channel compression method based on the correlation and coherence analysis of neural network inputs and outputs was proposed. Minimum load signal segment division criteria were introduced, and the minimum segment duration for each road surface was determined to compress the training dataset. Through continuous model iterations, the predicted values of the wheel six-component forces closely match the measured values, and the load characteristics are preserved. This demonstrates that the minimal dataset model can achieve a high level of prediction accuracy with fewer input channels and shorter load segment durations, resulting in a 28.85% improvement in computational efficiency.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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