<正>Gears alternately mesh and detach in driving process, and then working conditions of gears are alternately changing, so they are easy to be spalled and worn. But because of the effect of additive gaussian measurement noises, the signal-to-noises ratio is low; their fault features are difficult to extract. This study aims to propose an approach of gear faults classification, using the cumulants and support vector machines. The cumulants can eliminate the additive gaussian noises, boost the signal-to-noises ratio. Generalisation of support vector machines as classifier, which is employed structural risk minimisation principle, is superior to that of conventional neural networks, which is employed traditional empirical risk minimisation principle. Support vector machines as the classifier, and the third and fourth order cumulants as input, gears faults are successfully recognized. The experimental results show that the method of fault classification combining cumulants with support vector machines is very e
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机译:New Support Vector Machines Study Findings Reported from Pandit Deendayal Energy University (Support vector machine for fast fault detection and classification in modern power systems using quarter cycle data)
机译:Research Conducted at Birla Institute of Technology and Science Pilani Has Provided New Information about Support Vector Machines (Support Vector Machine for Misalignment Fault Classification Under Different Loading Conditions Using …)