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首页> 外文期刊>Proceedings of the Institution of Mechanical Engineers, Part J. Journal of engineering tribology >A fault diagnosis method combined with compound multiscale permutation entropy and particle swarm optimization-support vector machine for roller bearings diagnosis
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A fault diagnosis method combined with compound multiscale permutation entropy and particle swarm optimization-support vector machine for roller bearings diagnosis

机译：一种故障诊断方法与复合多尺度排列熵和粒子群优化 - 支持向量机用于滚子轴承诊断

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摘要

A method based on compound multiscale permutation entropy, support vector machine, and particle swarm optimization for roller bearings fault diagnosis was presented in this study. Firstly, the roller bearings vibration signals under different conditions were decomposed into permutation entropy values by the multiscale permutation entropy and compound multiscale permutation entropy methods. The compound multiscale permutation entropy model combined the different graining sequence information under each scale factor. The average value of each scale factor was regarded as the final entropy value in the compound multiscale permutation entropy model. The compound multiscale permutation entropy model suppressed the shortcomings of poor stability caused by the length of the original signals in the multiscale permutation entropy model. Validity and accuracy are considered in the numerical experiments, and then compared with the computational efficiency of the multiscale permutation entropy method. Secondly, the entropy values of the multiscale permutation entropy/compound multiscale permutation entropy under different scales are regarded as the input of the particle swarm optimization-support vector machine models for fulfilling the fault identification, the classification accuracy is used to verify the effectiveness of the multiscale permutation entropy/compound multiscale permutation entropy with particle swarm optimization-support vector machine. Finally, the experimental results show that the classification accuracy of the compound multiscale permutation entropy model is higher than that of the multiscale permutation entropy.
机译：本研究介绍了一种基于复合多尺寸置换熵，支持向量机和粒子群的粒子群优化的方法。首先，通过多尺度排列熵和复合多尺度置换熵方法将不同条件下的滚子轴承振动信号分解成置换熵值。复合多尺度置换熵模型将不同的粗制序列信息组合在每个比例因子下。每个比例因子的平均值被认为是复合多尺度置换熵模型中的最终熵值。复合多尺度置换熵模型抑制了多尺度置换熵模型中原始信号长度引起的稳定性差的缺点。在数值实验中考虑有效性和准确性，然后与多尺度置换熵方法的计算效率进行比较。其次，在不同尺度下的多尺度置换熵/复合多尺度置换熵的熵值被认为是用于满足故障识别的粒子群优化 - 支持向量机模型的输入，分类精度用于验证效果多尺度置换熵/复合多尺度置换熵与粒子群优化 - 支持向量机。最后，实验结果表明，复合多尺度置换熵模型的分类精度高于多尺度排列熵的分类精度。

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