A New Engine Fault Diagnosis Method Based on Multi-Sensor Data Fusion
A New Engine Fault Diagnosis Method Based on Multi-Sensor Data Fusion
复制标题
基于多传感器数据融合的发动机故障诊断新方法
DOI：
10.3390/app7030280
复制
发表时间：
2017-03-01
影响因子：
2.7
通讯作者：
Xie, Chunhe
中科院分区：
文献类型：
--
作者：
Jiang, Wen;Hu, Weiwei;Xie, Chunhe
故障诊断是现代工业中一个重要的研究方向。提出了一种基于多传感器数据融合的故障诊断新方法，采用D-S证据理论对不确定性进行建模。首先，利用传感器的观测值建立高斯型故障模型和测试模型。模型确定后，将测试模型与故障模型的相交区域转化为一组基本概率分配，并采用加权平均组合方法对得到的基本概率分配进行联合收割机组合。最后，通过给定的决策规则，得到诊断结果。利用Iris数据集和电机转子实测数据对本文提出的方法进行了验证，验证了所提方法的有效性。
Fault diagnosis is an important research direction in modern industry. In this paper, a new fault diagnosis method based on multi-sensor data fusion is proposed, in which the Dempster-Shafer (D-S) evidence theory is employed to model the uncertainty. Firstly, Gaussian types of fault models and test models are established by observations of sensors. After the models are determined, the intersection area between test model and fault models is transformed into a set of BPAs (basic probability assignments), and a weighted average combination method is used to combine the obtained BPAs. Finally, through some given decision making rules, diagnostic results can be obtained. The proposed method in this paper is tested by the Iris data set and actual measurement data of the motor rotor, which verifies the effectiveness of the proposed method.