【摘要】 针对传统粒子群优化算法以准确率或误判率作为适应度函数耗时长和轴向柱塞泵故障机制较为复杂的问题，提出一种基于改进适应度函数的Lévy飞行量子粒子群优化(QPSO)多分类相关向量机(MRVM)的轴向柱塞泵概率性智能软状态判别方法。为了克服人为设定核参数不精确、效率低等缺点，采用基于Lévy飞行的QPSO搜索MRVM的最优核参数；为了缩短寻优时间，将样本间余弦相似度作为寻优算法的适应度函数，并利用UCI机器学习标准数据集进行仿真来验证改进后优化方法的有效性及优越性；采集柱塞泵不同故障状态的数据，提取时频域和时域特征，输入到优化后的MRVM中，进行训练及测试。实验结果表明：所提方法可以有效提高故障诊断的准确率及诊断效率，同时能够实现软分类，即以概率形式输出诊断结果，能够为设备检修及维护提供可靠且符合实际的故障信息。更多还原

【Abstract】 In order to solve the problem that the traditional particle swarm optimization algorithm takes a long training time with accuracy or misjudgment rate as fitness function and the fault mechanism of axial piston pump is complex, a probabilistic intelligent state discrimination soft method of axial piston pump based on fitness function improved Lévy flying quantum-behaved particle swarm optimization(QPSO) multi-classification relevance vector machine(MRVM) was proposed.In order to overcome the shortcomings of imprecision and low efficiency by use of manual search for kernel parameters, QPSO based on Lévy flight was used to find the optimal kernel parameters of MRVM.In order to shorten the optimization time, the cosine similarity between samples was taken as the fitness function of the optimization algorithm, and simulation experiments were carried out by using of UCI machine learning standard data sets to verify the effectiveness and superiority of the improved method.The data of the piston pump under different fault conditions were sampled, and the time frequency domain and time domain features were extracted and input into the optimized MRVM for training and testing.The experimental results show that the proposed method can be used to effectively improve the accuracy and efficiency of fault diagnosis; at the same time, the soft classification is realized, that is, the diagnosis results can be output in the form of probability, which provides reliable and practical fault information for equipment repair and maintenance.更多还原