结合 SVM和改进证据理论的多信息融合故障诊断 / Multi-information fusion fault diagnosis using SVM & improved evidence theory
- Resource Type
- Academic Journal
- Authors
- 向阳辉; 张干清; 庞佑霞; XIANG Yang-hui; ZHANG Gan-qing; PANG You-xia
- Source
- 振动与冲击 / Journal of Vibration and Shock. (13):71-77
- Subject
- 支持向量机
证据理论
故障诊断
多信息融合
support vector machine(SVM)
evidence theory
fault diagnosis
multi-information fusion
- Language
- Chinese
- ISSN
- 1000-3835
为了综合合理利用设备多个方面特征信息来提高故障诊断的准确性,提出一种结合支持向量机(Support vector machine,SVM)和改进证据理论的多信息融合故障诊断方法。该方法通过混淆矩阵获取各 SVM局部诊断证据对各故障模式的可靠度,赋予不同的权重系数,并对由各 SVM局部诊断硬输出判决矩阵构造出的基本概率分配进行加权处理,从而实现 SVM和改进证据理论在多信息融合故障诊断中的有效结合。实验结果表明,各 SVM局部诊断证据的加权融合处理能够显著降低各局部诊断间的冲突,所提方法可以有效提高故障诊断的准确率。
In order to comprehensively and reasonably utilize much feature information of equipments to improve the accuracy of fault diagnosis,a method of multi-information fusion fault diagnosis was proposed based on support vector machine(SVM)and improved evidence theory.The reliability of local diagnosis evidence of each SVM for every failure mode was acquired with a confusion matrix to give different weight coefficient.The basic probability assignments constructed with a hard output decision matrix from the local diagnosis of each SVM were processed weightedly to realize the effective combination of SVM and improved evidence theory in multi-information fusion fault diagnosis. The experimental results showed that the weighted fusion treatment of local diagnosis evidences of all SVMs can significantly reduce the conflicts between local diagnoses;and the proposed method can effectively improve the accuracy of fault diagnosis.