稳健性(进化)
杠杆(统计)
干扰(通信)
人工智能
保险丝(电气)
融合
计算机科学
模式识别(心理学)
振动
传感器融合
信息融合
图像融合
工程类
机器学习
图像(数学)
电气工程
物理
频道(广播)
哲学
基因
量子力学
生物化学
化学
语言学
计算机网络
作者
Xin Li,Jian Cheng,Haidong Shao,Kan Liu,Baoping Cai
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2021-11-13
卷期号:18 (8): 5180-5189
被引量:33
标识
DOI:10.1109/tii.2021.3125385
摘要
Vibration signals and infrared images have different advantages and characteristics. Although a few recent researches have explored their information fusion in rotating machinery fault diagnosis, they show limited performance when facing strong interference and imbalanced cases. Therefore, a fusion framework based on confidence weight support matrix machine (CWSMM) is proposed. In this framework, CWSMM can not only fully leverage the structure information of infrared thermography images and vibration time–frequency images, but also has the following novelties. First, CWSMM uses dynamic penalty factors for different class samples to address the class imbalance problem. Second, by using the prior knowledge of matrix samples, a confidence weight assignment strategy is designed for CWSMM to improve the robustness. Last, the Dempster–Shafer (D-S) evidence theory is applied to fuse the posterior probability outputs of CWSMMs using different measurements. Experiment results demonstrate that the proposed method has promising fault diagnosis performance, specifically under strong interference and imbalanced datasets.
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