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- W2336561019 abstract "To recognize different bearing fault patterns under different operating conditions, a novel multi-team competitive optimization (MTCO) algorithm is proposed. The algorithm is inspired by the competitive behaviors among multiple teams. In the structure, it consists of a three-level optimization organization structure, so that the more potential optimal areas can be searched. Meanwhile, some new strategies imitating human thinking mode are proposed to increase the diversity and guide the members jumping out of location optimal areas, which include acceptable vector designed for imitating some uncertain or random events influencing the decision-making, betrayal mechanism and replacement mechanism designed for ensuring the reasonable turnover of staffs and leaders. Moreover, to reduce fault pattern recognition errors caused by inseparability of data with nonlinear distribution, a kernel function is introduced to increase the separability of data and improve recognition accuracies. Finally, the MTCO algorithm has been applied to diagnose real-world bearings faults. In this case, the MTCO algorithm has proved to have better recognition accuracies and to be effective." @default.
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- W2336561019 date "2016-01-01" @default.
- W2336561019 modified "2023-09-26" @default.
- W2336561019 title "A multi-team competitive optimization algorithm for bearing fault diagnosis" @default.
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- W2336561019 doi "https://doi.org/10.1109/rams.2016.7447990" @default.
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