Time Series Forecasting Based on Novel Support Vector Machine Using Artificial Fish Swarm Algorithm

2008 ◽  
Author(s):  
Xuejun Chen ◽  
Jianzhou Wang ◽  
Donghuai Sun ◽  
Jinzhao Liang
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Time Series Forecasting ◽  
Support Vector ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

scholarly journals Diagnosis of ball-bearing faults using support vector machine based on the artificial fish-swarm algorithm

2019 ◽  
Vol 39 (4) ◽  
pp. 954-967 ◽  
Author(s):  
Chih-Jer Lin ◽  
Wen-Lin Chu ◽  
Cheng-Chi Wang ◽  
Chih-Keng Chen ◽  
I-Ting Chen
Keyword(s):  
Support Vector Machine ◽  
Ball Bearing ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Ring Rolling ◽  
Support Vector ◽  
Model Parameters ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

Ball bearings are important parts of all modern rotating machines. Their function is to reduce friction, support rotating shafts and spindles, and bear loads. Bearing damage can result in abnormal vibrations, cause machine malfunction, and even be dangerous. In this study, analysis of four different ball-bearing conditions was carried out: normal bearings and bearings with inner ring, rolling body, and outer ring malfunction. This was based on electromechanical vibration signals produced on a fault diagnosis simulation platform. The objective was to use a series of signal processing analytical methods to build a set of identification models used to forecast malfunction. Wavelet packet transform technology was first used to process the vibration signal. The signals were pre-processed and analyzed before eigenvalue calculation was done to analyze the signal changes which allowed determination of the nature of the bearing malfunction to be made. The extracted eigenvalues and ball-bearing status categories were input to the support vector machine for model training and testing. Finally, the constructed model parameters were integrated with particle swarm optimization, and the artificial fish-swarm algorithm was used to obtain the optimal parameters for the classifier, and this improved the accuracy of malfunction classification.

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