A fault detection method for FADS system based on interval-valued neutrosophic sets, belief rule base, and D-S evidence reasoning

2021 ◽  
Vol 114 ◽  
pp. 106758
Author(s):  
Qianlei Jia ◽  
Jiayue Hu ◽  
Weiguo Zhang
2013 ◽  
Vol 26 (3) ◽  
pp. 717-729 ◽  
Author(s):  
Xin Zhao ◽  
Shicheng Wang ◽  
Jinsheng Zhang ◽  
Zhiliang Fan ◽  
Haibo Min

2016 ◽  
Vol 15 (06) ◽  
pp. 1345-1366 ◽  
Author(s):  
Hua Zhu ◽  
Jianbin Zhao ◽  
Yang Xu ◽  
Limin Du

In this paper, an interval-valued belief rule inference methodology based on evidential reasoning (IRIMER) is proposed, which includes the interval-valued belief rule representation scheme and its inference methodology. This interval-valued belief rule base is designed with interval-valued belief degrees embedded in both the consequents and the antecedents of each rule, which can represent uncertain information or knowledge more flexible and reasonable than the previous belief rule base. Then its inference methodology is developed on the interval-valued evidential reasoning (IER) approach. The IRIMER approach improves and extends the recently uncertainty inference methods from the rule representation scheme and the inference framework. Finally, a case is studied to demonstrate the concrete implementation process of the IRIMER approach, and comparison analysis shows that the IRIMER approach is more flexible and effective than the RIMER [J. B. Yang, J. Liu, J. Wang, H. S. Sii and H. W. Wang, Belief rule-base interference methodology using the evidential reasoning approach-RIMER, IEEE Transaction on Systems Man and Cybernetics Part A-Systems and Humans36 (2006) 266–285.] approach and the ERIMER [J. Liu, L. Martínez, A. Calzada and H. Wang, A novel belief rule base representation, generation and its inference methodology, Knowledge-Based Systems 53 (2013) 129–141.] approach.


Author(s):  
Weihai Sun ◽  
Lemei Han

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.


2010 ◽  
Vol 24 (2) ◽  
pp. 131-136 ◽  
Author(s):  
Ze Cheng ◽  
Bingfeng Li ◽  
Li Liu ◽  
Yanli Liu

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