scholarly journals Fuzzy-controlled Genetic Algorithm for Fault Detection in Distributed Systems

2019 ◽  
Author(s):  
Krishna Prasad
Keyword(s):  
Genetic Algorithm ◽  
Distributed Systems ◽  
Fault Detection

scholarly journals Fault Detection of Diesel Engine Air and after-Treatment Systems with High-Dimensional Data: A Novel Fault-Relevant Feature Selection Method

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 259
Author(s):  
Qilan Ran ◽  
Yedong Song ◽  
Wenli Du ◽  
Wei Du ◽  
Xin Peng
Keyword(s):  
Genetic Algorithm ◽  
Diesel Engine ◽  
Fault Detection ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Diesel Engines ◽  
Canonical Correlation ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Relevant Variables

In order to reduce pollutants of the emission from diesel vehicles, complex after-treatment technologies have been proposed, which make the fault detection of diesel engines become increasingly difficult. Thus, this paper proposes a canonical correlation analysis detection method based on fault-relevant variables selected by an elitist genetic algorithm to realize high-dimensional data-driven faults detection of diesel engines. The method proposed establishes a fault detection model by the actual operation data to overcome the limitations of the traditional methods, merely based on benchmark. Moreover, the canonical correlation analysis is used to extract the strong correlation between variables, which constructs the residual vector to realize the fault detection of the diesel engine air and after-treatment system. In particular, the elitist genetic algorithm is used to optimize the fault-relevant variables to reduce detection redundancy, eliminate additional noise interference, and improve the detection rate of the specific fault. The experiments are carried out by implementing the practical state data of a diesel engine, which show the feasibility and efficiency of the proposed approach.

scholarly journals Analysis of Asymmetric Piecewise Linear Stochastic Resonance Signal Processing Model Based on Genetic Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Lina He ◽  
Chuan Jiang
Keyword(s):  
Genetic Algorithm ◽  
Fault Detection ◽  
Stochastic Resonance ◽  
Signal To Noise Ratio ◽  
Piecewise Linear ◽  
Poor Performance ◽  
System Model ◽  
Adaptive Genetic Algorithm ◽  
Resonance System ◽  
Noise Energy

The stochastic resonance system has the advantage of making the noise energy transfer to the signal energy. Because the existing stochastic resonance system model has the problem of poor performance, an asymmetric piecewise linear stochastic resonance system model is proposed, and the parameters of the model are optimized by a genetic algorithm. The signal-to-noise ratio formula of the model is derived and analyzed, and the theoretical basis for better performance of the model is given. The influence of the asymmetric coefficient on system performance is studied, which provides guidance for the selection of initial optimization range when a genetic algorithm is used. At the same time, the formula is verified and analyzed by numerical simulation, and the correctness of the formula is proved. Finally, the model is applied to bearing fault detection, and an adaptive genetic algorithm is used to optimize the parameters of the system. The results show that the model has an excellent detection effect, which proves that the model has great potential in fault detection.

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