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This paper proposes a highly reliable fault diagnosis approach for low-speed bearings. The proposed approach first extracts wavelet-based fault features that represent diverse symptoms of multiple low-speed bearing defects. The most useful fault features for diagnosis are then selected by utilizing a genetic algorithm (GA)-based kernel discriminative feature analysis cooperating with one-against-all...
Under the framework of evolutionary paradigms, many variations of evolutionary algorithms have been designed. Each of the algorithms performs well in certain cases and none of them are dominating one another. This study is based on the idea of synthesizing different evolutionary algorithms so as to complement the limitations of each algorithm. On top of this idea, this paper proposes an adaptive mechanism...
In this paper, a new memetic algorithm (MA) for multiobjective (MO) optimization is proposed, which combines the global search ability of particle swarm optimization with a synchronous local search heuristic for directed local fine-tuning. A new particle updating strategy is proposed based upon the concept of fuzzy global-best to deal with the problem of premature convergence and diversity maintenance...
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