Simulation and parameter optimization of flux cored arc welding using artificial neural network and particle swarm optimization algorithm

2012 ◽  
Vol 25 (1) ◽  
pp. 67-76 ◽  
Author(s):  
D. Katherasan ◽  
Jiju V. Elias ◽  
P. Sathiya ◽  
A. Noorul Haq
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Swarm Optimization ◽  
Parameter Optimization ◽  
Optimization Algorithm ◽  
Arc Welding ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Flux Cored Arc Welding

scholarly journals Fault Diagnosis of Plunger Pump in Truck Crane Based on Relevance Vector Machine with Particle Swarm Optimization Algorithm

2013 ◽  
Vol 20 (4) ◽  
pp. 781-792 ◽  
Author(s):  
Wenliao Du ◽  
Ansheng Li ◽  
Pengfei Ye ◽  
Chengliang Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Plunger Pump ◽  
Classical Models

Promptly and accurately dealing with the equipment breakdown is very important in terms of enhancing reliability and decreasing downtime. A novel fault diagnosis method PSO-RVM based on relevance vector machines (RVM) with particle swarm optimization (PSO) algorithm for plunger pump in truck crane is proposed. The particle swarm optimization algorithm is utilized to determine the kernel width parameter of the kernel function in RVM, and the five two-class RVMs with binary tree architecture are trained to recognize the condition of mechanism. The proposed method is employed in the diagnosis of plunger pump in truck crane. The six states, including normal state, bearing inner race fault, bearing roller fault, plunger wear fault, thrust plate wear fault, and swash plate wear fault, are used to test the classification performance of the proposed PSO-RVM model, which compared with the classical models, such as back-propagation artificial neural network (BP-ANN), ant colony optimization artificial neural network (ANT-ANN), RVM, and support vectors, machines with particle swarm optimization (PSO-SVM), respectively. The experimental results show that the PSO-RVM is superior to the first three classical models, and has a comparative performance to the PSO-SVM, the corresponding diagnostic accuracy achieving as high as 99.17% and 99.58%, respectively. But the number of relevance vectors is far fewer than that of support vector, and the former is about 1/12–1/3 of the latter, which indicates that the proposed PSO-RVM model is more suitable for applications that require low complexity and real-time monitoring.

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