相关向量机及其在故障诊断与预测中的应用

相关向量机(RVM)是一种基于稀疏Bayesian学习理论的新型机器学习方法,具有概率式输出、稀疏性强、参数设置简单、核函数选择灵活等优点,克服了人工神经网络(ANN)和支持向量机(SVM)等典型机器学习方法的诸多固有缺陷。文章从模型选择与优化、模型计算效率和模型鲁棒性改进3个方面综述了RVM的理论研究进展;总结了RVM在故障诊断与预测中的应用研究现状;分析指出了当前研究中存在的问题,并讨论了基于RVM的故障诊断与预测技术的研究方向。

Relevance Vector Machine and Its Applications in Fault Diagnosis and Prognosis

Relevance vector machine （RVM） is a new machine learning method based on sparse Bayesian learning theory, which has probabilistic oulputs, high sparsity, simple parameter tuning and flexible selection of kernel fimction. RVM has overcome many inherent defects of typical machine learning methods, such as ANN and SVM. The research progress of relevance vector machine （RVM） was summarized in model selection and optimization, model computational efficiency and model robustness improvement. The research status of applications of RVM iu fault diagnosis and prognosis was introduced. The existing problems in the current research were analyzed and the development trends of fauh diagnosis and prognosis based on RVM were discussed.