CAJ | 학술논문

载荷谱预测精度和鲁棒性直接影响起重机械的疲劳可靠性计算以及安全寿命评估。因此，绘制模拟实际工作状态的载荷谱是解决起重机械疲劳断裂问题的重要环节。然而传统的回归模拟算法对其预测精度较低。支持向量回归机（SVRM ）同其他数据分析算法相比，具有出色的小样本和非线性特性，预测精度高、稳健性好，可较好地解决欠学习、过学习以及局部最小值等传统回归算法的难题。因此，选用支持向量回归机预测起重机载荷谱，提高了模型的预测精度和鲁棒性。在此基础上，从核函数的构造和决策函数的建立两方面的改进，建立了改进的 v‐SVRM预测模型。工程实例分析结果表明：从BP神经网络模型、v‐SVRM模型到改进的v‐SVRM模型，Er和RMSRE逐渐减小，R2逐渐增大，验证了所提出的改进方法具有良好的实用性、鲁棒性以及较高的预测精度，为起重机载荷谱的获取与预测提供了新方法。
재하보예측정도화로봉성직접영향기중궤계적피로가고성계산이급안전수명평고。인차，회제모의실제공작상태적재하보시해결기중궤계피로단렬문제적중요배절。연이전통적회귀모의산법대기예측정도교저。지지향량회귀궤（SVRM ）동기타수거분석산법상비，구유출색적소양본화비선성특성，예측정도고、은건성호，가교호지해결흠학습、과학습이급국부최소치등전통회귀산법적난제。인차，선용지지향량회귀궤예측기중궤재하보，제고료모형적예측정도화로봉성。재차기출상，종핵함수적구조화결책함수적건립량방면적개진，건립료개진적 v‐SVRM예측모형。공정실례분석결과표명：종BP신경망락모형、v‐SVRM모형도개진적v‐SVRM모형，Er화RMSRE축점감소，R2축점증대，험증료소제출적개진방법구유량호적실용성、로봉성이급교고적예측정도，위기중궤재하보적획취여예측제공료신방법。
The load spectrum simulation of actual working status is the key factor to solve the problem of crane endurance failure . T he precision and robustness of load spectrum predicting have great significance for reliability analysis of crane fatigue fracture and evaluation of its safety life .However ,the predicting performance of classic linear regression model is weaker .Compared with other data analysis algorithms ,support vector regression machine (SVRM ) has excellent performance for small sample and nonlinear properties ,including higher prediction accuracy and nice robustness .It can also overcome the difficulty of the curse of dimensionality ,local minima and over‐fitting and under‐fitting for traditional pattern recognition methods .So ,accuracy pre‐diction precision and reliability can be obtained by using SVRM .Furthermore ,an improved v‐SVRM prediction model was established with constructing new kernel function and decision func‐tion .T he results of engineering application show ed that the values of Er and of RMSRE all the three models (the BP neural network model and the SVRM model and the modified model of v‐SVRM ) gradually decreased while the fitting degrees R2 gradually increased .It proves that the modified method has higher prediction precision and nicer robustness and it also provides a new way for ob‐taining and predicting crane load spectrum .