NARX model-based dynamic parametrical model identification of the rotor system with bolted joint

Author(s):  
Yuqi Li ◽  
Zhong Luo ◽  
Baolong Shi ◽  
Fengxia He
Author(s):  
Jiye Shao ◽  
Rixin Wang ◽  
Jingbo Gao ◽  
Minqiang Xu

The rotor is one of the most core components of the rotating machinery and its working states directly influence the working states of the whole rotating machinery. There exists much uncertainty in the field of fault diagnosis in the rotor system. This paper analyses the familiar faults of the rotor system and the corresponding faulty symptoms, then establishes the rotor’s Bayesian network model based on above information. A fault diagnosis system based on the Bayesian network model is developed. Using this model, the conditional probability of the fault happening is computed when the observation of the rotor is presented. Thus, the fault reason can be determined by these probabilities. The diagnosis system developed is used to diagnose the actual three faults of the rotor of the rotating machinery and the results prove the efficiency of the method proposed.


2009 ◽  
Vol 42 (10) ◽  
pp. 1074-1079 ◽  
Author(s):  
Luigi Piroddi ◽  
Marcello Farina ◽  
Marco Lovera

2014 ◽  
Vol 2 (2) ◽  
pp. 11-14
Author(s):  
Sang-Hyun Lee ◽  
Dae-Won Park ◽  
Kyung-Il Moon

2008 ◽  
Vol 41 (2) ◽  
pp. 2726-2731 ◽  
Author(s):  
Luigi Piroddi ◽  
Marco Lovera

2019 ◽  
Vol 89 (11) ◽  
pp. 2381-2395 ◽  
Author(s):  
Yuqi Li ◽  
Zhong Luo ◽  
Zijia Liu ◽  
Xiaojie Hou

2017 ◽  
Vol 7 (9) ◽  
pp. 911 ◽  
Author(s):  
Ying Ma ◽  
Haopeng Liu ◽  
Yunpeng Zhu ◽  
Fei Wang ◽  
Zhong Luo

2018 ◽  
Vol 20 (5) ◽  
Author(s):  
Moustafa M. A. Ibrahim ◽  
Rikard Nordgren ◽  
Maria C. Kjellsson ◽  
Mats O. Karlsson

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