To overcome the shortcomings that the early fault characteristics of rolling bearing are not easy to be extracted and the identification accuracy is not high enough, a novel collaborative diagnosis method is presented combined with VMD and LSSVM for incipient faults of rolling bearing. First, the basic concept of VMD was introduced in detail, and then, the adaptive selection principle of parameter K in VMD was constructed by instantaneous frequency mean. Furthermore, we used Lagrangian polynomial and Euclidean norm to verify the value of K accurately. Secondly, we proposed a classification algorithm based on PSO-optimized LSSVM. Meanwhile, the flowchart of the classification algorithm of fault modes may be also designed. Third, the experiment shows that the presented algorithm in this paper is effective by using the existing failure data provided by the laboratory of Guangdong Petrochemical Research Institute. Finally, some conclusions and application prospects were discussed.

中文翻译：

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基于VMD参数优化的LSSVM和PSO集成的故障模式分类算法

针对滚动轴承早期故障特征不易提取，识别精度不够高的缺点，提出了一种结合VMD和LSSVM的滚动轴承初发故障协同诊断方法。首先详细介绍了VMD的基本概念，然后通过瞬时频率均值构造了VMD中参数K的自适应选择原理。此外，我们使用拉格朗日多项式和欧几里得范数来验证K的值准确。其次，提出了一种基于PSO优化的LSSVM的分类算法。同时，还可以设计故障模式分类算法的流程图。第三，实验表明，本文提出的算法利用广东石化研究院实验室提供的现有故障数据是有效的。最后，讨论了一些结论和应用前景。