Unified model-based fault diagnosis for three industrial application studies

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.

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Development of a robust model-based fault diagnosis technique for Reusable Launch Vehicles. A case study

IFAC Proceedings Volumes ◽

10.3182/20080706-5-kr-1001.02037 ◽

2008 ◽

Vol 41 (2) ◽

pp. 12036-12041 ◽

Cited By ~ 4

Author(s):

Alexandre Falcoz ◽

David Henry ◽

Ali Zolghadri

Keyword(s):

Fault Diagnosis ◽

Launch Vehicles ◽

Reusable Launch Vehicles ◽

Model Based ◽

Robust Model ◽

Diagnosis Technique

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The Application of Local Mean Decomposition and Variable Predictive Model-Based Class Discriminate in Gear Fault Diagnosis

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1014.510 ◽

2014 ◽

Vol 1014 ◽

pp. 510-515 ◽

Cited By ~ 1

Author(s):

You Cai Xu ◽

Xin Shi Li ◽

Ran Tao ◽

Shu Guo ◽

Min Gou ◽

...

Keyword(s):

Fault Diagnosis ◽

Predictive Model ◽

Feature Vector ◽

Local Mean Decomposition ◽

Outer Race ◽

Model Based ◽

Gear Fault ◽

Local Mean ◽

Gear Fault Diagnosis ◽

Non Stationary Signal

The time-domain energy message conveyed by vibration signals of different gear fault are different, so a method based on local mean decomposition (LMD) and variable predictive model-based class discriminate (VPMCD) is proposed to diagnose gear fault model. The vibration signal of gear which is the research object in this paper is decomposed into a series of product functions (PF) by LMD method. Then a further analysis is to select the PF components which contain main fault information of gear, the energy feature parameters of the selected PF components are used to form a fault feature vector. The variable predictive model-based class discriminate is a new multivariate classification approach for pattern recognition, through taking fully advantages of the fault feature vector. Finally, gear fault diagnosis is distinguished into normal state, inner race fault and outer race fault. The results show that LMD method can decompose a complex non-stationary signal into a number of PF components whose frequency is from high to low. And the method based on LMD and VPMCD has a high fault recognition function by analyzing the fault feature vector of PF.

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