计算机科学 ›› 2011, Vol. 38 ›› Issue (1): 268-270.
• 人工智能 • 上一篇 下一篇
巫茜,蔡海尼,黄丽丰
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WU Qian,CAI Hai-ni,HUANG Li-feng
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摘要: 目前故障诊断的实际应用中,因噪声的千扰,基于单传感器的故障诊断稳定性较差,很难达到满意的诊断精确度。提出了一种多传感器多特征数据融合的故障诊断方法。该方法利用多传感器从不同部位获取同一部件的运行状况,并通过构建多源特征融合模型,提高特征信息的抗干扰性,最后通过融合特征信息来完成部件的故障诊断。在将新方法应用于滚动轴承故障诊断的试验中,可以看到新方法能够获得较好的性能,比基于单传感器故障诊断的精确度更高。
关键词: 多源信息融合,故障诊断,主成分分析,特征级,分类
Abstract: In most of current application,fault diagnosis based on single sensor show bad performance and is difficult to achieve satisfactory accuracy owning to noises. In this paper,a multi-sensor multi-feature fusion fault diagnosis method was proposed. The method firstly collects part operating conditions using multiple sensors installed on it, Then a fusion model based on principal component analysis was constructed to fuse all extracted features from the different sensors.Finally,fault diagnosis was carried out according to the fused results. Experiments and results show that new method can achieve perfect performance which is also better than that achieved by singlcscnsor.
Key words: Feature fusion,Fault diagnosis,PCA,Character level,Classifier
巫茜,蔡海尼,黄丽丰. 基于主成分分析的多源特征融合故障诊断方法[J]. 计算机科学, 2011, 38(1): 268-270. https://doi.org/
WU Qian,CAI Hai-ni,HUANG Li-feng. Feature-level Fusion Fault Diagnosis Based on PCA[J]. Computer Science, 2011, 38(1): 268-270. https://doi.org/
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