相关向量机及其在机械故障诊断中的应用研究进展

相关向量机是一种基于贝叶斯稀疏理论的新型机器学习算法，近年来被应用于多个行业，并得到了国内外学者的不断关注和研究，然而在机械故障诊断领域还未得到足够的重视。简述了相关向量机的特点，通过与支持向量机相比较，阐述了相关向量机的优缺点，综述和分析了近年来相关向量机的国内外研究现状，重点关注相关向量机在机械设备状态监测与故障诊断领域的研究进展。在此基础上，分析了相关向量机研究所存在的一些问题，并展望了相关向量机在机械故障诊断领域应用的未来方向。

Relevance vector machine and its applications in machine fault diagnosis

Relevance vector machine (RVM) is a new statistical machine learning algorithm based on the theory of the sparse Bayesian learning and it has been applied to many fields in recent years. The researchers both at home and abroad have paid more and more attention on RVM. However, it has not been placed a high value in the mechanical fault diagnostic research. The characteristics of RVM are stated, the advantages and disadvantages compared with Support vector machine (SVM) are discussed, and the inland and overseas research advances are reviewed, especially in the field of mechanical fault diagnostic. Moreover, some existing problems in current research are analyzed, and the research directions of RVM for the mechanical fault diagnosis in the future are prospected.