Abstract
AbstractTo diagnose the bearing faults, both local mean mode decomposition and probabilistic neural networks were proposed to use. A new criterion for the sifting process to stop was given to make this method more efficient. Calculated the average of instantaneous frequency and the ratio of energy of the decomposed parts by local mean mode decomposition, and used the probabilistic neural networks to classify it. The results indicated that this method is precise and valid. Therefore, it develops the intelligent fault diagnosis.KeywordsFault DiagnosisEmpirical Mode DecompositionInstantaneous FrequencyRolling BearingProbabilistic Neural NetworkThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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