A Novel Approximate Model Based Fault Diagnosis Technique for a Photovoltaic DC/AC Grid Tied Inverter

2019 ◽  
Author(s):  
Laurice Ann L. Smith ◽  
Satya Naidu ◽  
Mohamed O. Badawy
Keyword(s):  
Fault Diagnosis ◽  
Approximate Model ◽  
Model Based ◽  
Diagnosis Technique

On-Board Model Based Fault Diagnosis for CubeSat Attitude Control Subsystem: Flight Data Results

2021 ◽  
Author(s):  
Ryan Mackey ◽  
Allen Nikora ◽  
Cornelia Altenbuchner ◽  
Robert Bocchino ◽  
Michael Sievers ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Attitude Control ◽  
Flight Data ◽  
Control Subsystem ◽  
Model Based

A fast MPPT-based anomaly detection and accurate fault diagnosis technique for PV arrays

2021 ◽  
Vol 234 ◽  
pp. 113950
Author(s):  
Chenxi Li ◽  
Yongheng Yang ◽  
Kanjian Zhang ◽  
Chenglong Zhu ◽  
Haikun Wei
Keyword(s):  
Fault Diagnosis ◽  
Anomaly Detection ◽  
Diagnosis Technique

Probabilistic Model-Based Fault Diagnosis of the Rotor System

2007 ◽  
Author(s):  
Jiye Shao ◽  
Rixin Wang ◽  
Jingbo Gao ◽  
Minqiang Xu
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Network Model ◽  
Rotating Machinery ◽  
Rotor System ◽  
Bayesian Network Model ◽  
Diagnosis System ◽  
Model Based ◽  
Core Components ◽  
Fault Diagnosis System

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.

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

2014 ◽  
Vol 1014 ◽  
pp. 510-515 ◽  
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.

scholarly journals Optimization of a Dynamic Fault Diagnosis Model Based on Machine Learning

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 65065-65077 ◽  
Author(s):  
Shigang Zhang ◽  
Xu Luo ◽  
Yongmin Yang ◽  
Long Wang ◽  
Xiaofei Zhang
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Model Based ◽  
Diagnosis Model ◽  
Dynamic Fault Diagnosis
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