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中文摘要：
针对齿轮箱振动信号的非线性和非平稳性,提出一种多重分形和粒子群优化的支持向量机（PSO-SVM）相结合的故障诊断方法。首先采用短时分维作为模糊控制参数的分形滤波器对背景噪声较大的齿轮箱振动信号进行滤波降噪;其次引入多重分形谱算法对滤波后信号进行分析,发现多重分形特征量Δa（q）、f（a（q））max、盒子维数Db能很好地反映齿轮箱工作状态;最后对支持向量机（SVM）的参数利用粒子群优化（PSO）算法进行优化,并将多重分形特征量分别作为SVM和PSOSVM的输入参数以识别齿轮箱故障。结果表明,基于粒子群优化的支持向量机可以提高分类正确率。同时证明了基于多重分形和PSO-SVM在齿轮箱故障诊断中的有效性。
英文摘要：
Aiming at the non-stationary and nonlinear of gearbox vibration signals, a fault diag- nosis method based on the multi-fractal and particle swarm optimization support vector machine （PSO -SVM） is put forward. Firstly, the fractal filter with short-time fractal dimension as fuzzy control parameters is used to filtering noise reduction the gearbox vibration signals with bigger background noi- ses. Secondly, the multi-fractal spectrum algorithm is applied to analyze the signal after filtering, the results show that the characteristic parameters： Aa（q）,f（a（q））m,x and box dimensions Db can give a good presentation for gearbox working condition. Finally, the particle swarm optimization （PSO） is applied to optimize the parameters of support vector machine （SVM）. Taking the muhi-fractal characteristic vectors as input parameters of PSO-SVM and SVM to recognize the fault types of the gearbox. The results show that SVM based on particle swarm optimization can improve the classification accuracy. Meanwhile, the validity of gearbox fault diagnosis based on muti-fractal and PSO-SVM is proved.
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