基于改进SBELM的耦合故障诊断方法

Simultaneous Fault Diagnosis Method Based on Improved Sparse Bayesian Extreme Learning Machine

为了对主减速器的耦合故障进行识别,通过对振动信号经过集成经验模态分解(ensemble empirical mode decomposition,EEMD)所获得的高频分量采用自适应阈值降噪和对低频分量采用区间阈值降噪,有效去除了信号噪声,创建了配对多标签分类策略(paired multi-label classification,PMLC).基于PMLC和稀疏贝叶斯极限学习机(sparse Bayesian extreme learning machine,SBELM)用单故障样本构造概率分类器集,再采用网格搜索方法生成最优决策阈值,将分类器集的概率输出转换为耦合故障模式,提出了基于自适应区间阈值降噪和SBELM的耦合故障诊断方法,并用主减速器的实际样本集验证了该方法的性能.研究结果表明:该方法的诊断精确度达到96.1%,比基于PNN(probability neural networks)和SVM(support vector machine)的诊断方法提高了5%;该方法的训练时间和执行时间为131.4和61.3 ms,比基于SVM的诊断方法减少了70%.

In order to identify simultaneous faults of the main reducer, an adaptive threshold denoising was adopted for intrinsic mode functions （IMFs） with high frequency and an interval threshold denoising was adopted for IMFs with low frequency, which are obtained from ensemble empirical mode decomposition （EEMD） of vibration signal, to eliminate noises. Then, a paired multi-label classification （PMLC） strategy was established, and probability classifiers based on PMLC and sparse Bayesian extreme learning machine （SBELM） were constructed with single fault samples; an optimal decision threshold was generated by using the grid searching method to convert the probability output obtained from classifiers into final simultaneous fault modes. On this basis, a simultaneous fault diagnosis （SFD） method based on adaptive threshold de-noising and SBELM was proposed. Its performance was verified using real samples of the main reducer. The experiment results show that the diagnostic accuracy of the method is 96. 1% , which is 5% higher than that of methods based on probabilistic neural network （PNN） and support vector machine （SVM）; its training time and execution time are 131.4 and 61.3ms, respectively, approximately 70% shorter than those of the method based on SVM.