关键词: 多维度排列熵, 支持向量机, 早期故障诊断, 滚动轴承

Abstract: Aiming at the problem that the early fault diagnosis of rolling bearing could not be carried out effectively by most exiting methods,the permutation entropy algorithm was used to analyze the early faults of bearing vibration signal.Through researching the impact of embedding dimension and delaying time on signal permutation entropy,a feature extraction method based on multi-dimension permutation entropy algorithm was proposed.The intelligent diagnosis model was established to diagnose bearing early faults according to the support vector machine theory and the features calculated by the proposed method.The fault data with different types and varying degrees of bearings were analyzed to prove the effectiveness of multi-dimension permutation entropy method for extracting the features information of bearing's different states effectively and the universality of proposed intelligent model for diagnosing the bearing early faults accurately.Under the circumstance of poor samples,the proposed model had- high diagnostic capability which was suitable for on-line monitoring for bearing early faults.

Key words: multi-dimension permutation entropy, support vector machine, early fault diagnosis, rolling bearing