Advanced fault diagnosis method for nuclear power plant based on convolutional gated recurrent network and enhanced particle swarm optimization

2021 ◽  
Vol 151 ◽  
pp. 107934
Author(s):  
Hang Wang ◽  
Min-jun Peng ◽  
Abiodun Ayodeji ◽  
Hong Xia ◽  
Xiao-kun Wang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Particle Swarm ◽  
Recurrent Network ◽  
Swarm Optimization ◽  
Diagnosis Method ◽  
Enhanced Particle Swarm Optimization

scholarly journals Nuclear Power Plant Construction Scheduling Problem with Time Restrictions: A Particle Swarm Optimization Approach

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Shang-Kuan Chen ◽  
Yen-Wu Ti ◽  
Kuo-Yu Tsai
Keyword(s):  
Particle Swarm Optimization ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Particle Swarm ◽  
Scheduling Problem ◽  
Swarm Optimization ◽  
Construction Scheduling ◽  
Plant Construction ◽  
Reactor Installation

In nuclear power plant construction scheduling, a project is generally defined by its dependent preparation time, the time required for construction, and its reactor installation time. The issues of multiple construction teams and multiple reactor installation teams are considered. In this paper, a hierarchical particle swarm optimization algorithm is proposed to solve the nuclear power plant construction scheduling problem and minimize the occurrence of projects failing to achieve deliverables within applicable due times and deadlines.

scholarly journals PARTICLE SWARM OPTIMIZATION BASED PROBABILISTIC NEURAL NETWORK FOR CLASSIFICATION OF SEVERE ACCIDENT OF NUCLEAR REACTOR

2021 ◽  
Vol 23 (3) ◽  
pp. 99
Author(s):  
Yoyok Dwi Setyo Pambudi
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Nuclear Reactor ◽  
Probabilistic Neural Network ◽  
Particle Swarm ◽  
Severe Accident ◽  
Swarm Optimization

Due to its danger and complexity, the identification and prediction of major severe accident scenarios from an initiating event of a nuclear power plant remains a challenging task. This paper aims to classify severe accident at the Advanced Power Reactor (APR) 1400, which includes the loss of coolant accidents (LOCA), total loss of feedwater (TLOFW), station blackout (SBO), and steam generator tube rupture (SGTR) using a standard  probabilistic neural network (PNN)  and Particle Swarm Optimization Based Probabilistic Neural Network (PSO PNN). The algorithm has been implemented in MATLAB.  The experiment results showed that supervised PNN PSO could classify severe accident of nuclear power plant better than the standar PNN.

scholarly journals Fault Diagnosis for Rolling Bearings Based on Fine-Sorted Dispersion Entropy and SVM Optimized with Mutation SCA-PSO

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 404 ◽  
Author(s):  
Wenlong Fu ◽  
Jiawen Tan ◽  
Yanhe Xu ◽  
Kai Wang ◽  
Tie Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Production Efficiency ◽  
Particle Swarm ◽  
Support Vector ◽  
Rolling Bearings ◽  
Swarm Optimization ◽  
Svm Model ◽  
Diagnosis Method ◽  
Sine Cosine Algorithm

Rolling bearings are a vital and widely used component in modern industry, relating to the production efficiency and remaining life of a device. An effective and robust fault diagnosis method for rolling bearings can reduce the downtime caused by unexpected failures. Thus, a novel fault diagnosis method for rolling bearings by fine-sorted dispersion entropy and mutation sine cosine algorithm and particle swarm optimization (SCA-PSO) optimized support vector machine (SVM) is presented to diagnose a fault of various sizes, locations and motor loads. Vibration signals collected from different types of faults are firstly decomposed by variational mode decomposition (VMD) into sets of intrinsic mode functions (IMFs), where the decomposing mode number K is determined by the central frequency observation method, thus, to weaken the non-stationarity of original signals. Later, the improved fine-sorted dispersion entropy (FSDE) is proposed to enhance the perception for relationship information between neighboring elements and then employed to construct the feature vectors of different fault samples. Afterward, a hybrid optimization strategy combining advantages of mutation operator, sine cosine algorithm and particle swarm optimization (MSCAPSO) is proposed to optimize the SVM model. The optimal SVM model is subsequently applied to realize the pattern recognition for different fault samples. The superiority of the proposed method is assessed through multiple contrastive experiments. Result analysis indicates that the proposed method achieves better precision and stability over some relevant methods, whereupon it is promising in the field of fault diagnosis for rolling bearings.

Fault diagnosis method based on supervised particle swarm optimization classification algorithm

2018 ◽  
Vol 22 (1) ◽  
pp. 191-210 ◽  
Author(s):  
Bo Zheng ◽  
Hong-Zhong Huang ◽  
Wei Guo ◽  
Yan-Feng Li ◽  
Jinhua Mi
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Particle Swarm ◽  
Classification Algorithm ◽  
Swarm Optimization ◽  
Diagnosis Method

Simulation for Automotive Engine Fault Diagnosis Method

2014 ◽  
Vol 687-691 ◽  
pp. 882-885
Author(s):  
Huan Xue Liu ◽  
Guang Dong Zhang ◽  
Zhen Zhong Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimization Method ◽  
Accurate Data ◽  
Swarm Optimization ◽  
Automotive Engine ◽  
Diagnosis Method

For engine fault diagnosis problem, an engine fault diagnosis method based on particle swarm optimization algorithm is proposed. The velocity and spatial position of all the particles in the particle swarm are updated, in order to provide accurate data basis for the engine fault diagnosis. Particle swarm optimization method is utilized to process iteration for all particles, so as to determine whether failure exists in components of engine. Experimental results show that with the proposed algorithm to diagnose engine fault can effectively improve the accuracy of fault diagnosis, and achieved the desired results.

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