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中文摘要：
针对非线性机械故障信号分离依赖于非线性函数的选取问题,提出一种基于自适应粒子群优化的机械故障特征提取方法.该方法把观测信号的负熵做为目标函数,通过观测信号的状态自适应地调整惯性因子,有效克服了信号恢复质量和收敛速度间的矛盾.通过对仿真信号的分离,实现了分离输出信号与仿真信号的一致性.最后利用该方法对实测混叠机械振动信号成功实现了故障信号分离,验证了所提方法的有效性.
英文摘要：
Aimed at the problem that the separation of nonlinear mechanical failure signal is dependent on the selection of non-linear function, an extraction method of mechanical fault characteristics is proposed based on adaptive particle swarm optimization. In this method, the negentropy of observed signals is taken as objective function. The inertia factor is adaptively adjusted on the basis of the condition of the observed signals, so that the contradiction between the convergence speed and recovery quality of the separated sig- nals will be overcome effectively. By means of separation of simulation signals, the consistence of separa- ted output signal with simulation signal is realized. Finally, the separation of fault signals from the mixed- piled mechanical vibration signals is realized successfully by using this method proposed.
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同项目期刊论文
Fault feature extraction of a rotor system based on local mean decomposition and Teager energy kurto
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