马文博,梅磊,刘波.基于ICS-LSSVM的包装机械动力机轴承的故障识别[J].包装工程,2018,39(11):176-181.
MA Wen-bo,MEI Lei,LIU Bo.Fault Identification of Power Machine Bearings of Packaging Machinery Based on ICS-LSSVM[J].Packaging Engineering,2018,39(11):176-181. |
| 基于ICS-LSSVM的包装机械动力机轴承的故障识别 |
| Fault Identification of Power Machine Bearings of Packaging Machinery Based on ICS-LSSVM |
| 投稿时间：2018-02-24 修订日期：2018-06-10 |
| DOI：10.19554/j.cnki.1001-3563.2018.11.031 |
| 中文关键词: 动力机 故障识别 参数寻优 最小二乘支持向量机 布谷鸟搜索算法 |
| 英文关键词: power machine fault identification parameter optimization least squares support vector machine cuckoo search algorithm |
| 基金项目:国家自然科学基金（61503181） |
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| 中文摘要: |
| 目的 针对包装机械设备中动力机轴承的故障诊断识别率低的问题，提出一种基于参数寻优的故障识别方法。方法 首先通过主元分析算法对包装设备动力机的振动数据进行主成分特征提取，减少各数据间的相关性，然后采用LSSVM对各类数据样本进行故障识别。为了克服LSSVM惩罚因子和核函数参数易出现局部最优、收敛精度差等问题，提出一种ICS算法优化LSSVM的状态参数，提高包装机械动力机轴承故障诊断的识别率，以实测糖果厂包装机械振动数据为例验证所提方法的有效性。结果 实验结果表明，在包装机械动力机轴承故障类别确定的情况下，算法能够高精度地识别各类动力机故障。结论 该算法实现了分类器参数的自适应选择，为提高包装机械动力机轴承故障诊断的识别率提供了可靠的方法。 |
| 英文摘要: |
| The work aims to propose a fault identification method based on parameter optimization with respect to the problem of low recognition rate of fault in power machine bearings of packaging machinery. Firstly, principal component analysis algorithm was used to extract the principal component of vibration data of power machine in packaging machinery and reduce the correlation between the data. Then, LSSVM was used to identify the fault in various kinds of data samples. In order to overcome the local extremum and poor convergence precision of LSSVM penalty factors and kernel function parameters, an ICS algorithm was proposed for the optimization of LSSVM state parameter to improve the recognition rate of power machine bearings in packaging machinery. Taking the measured vibration data of packaging machinery in candy factory as an example, the validity of the proposed method was verified. Experimental results showed that the algorithm could identify the fault in all kinds of power machines with high precision when the type of fault in power ma-chine bearing of packaging machinery. The proposed algorithm realizes the adaptive selection of the classifier parameters, and provides a reliable method for improving the recognition rate of fault diagnosis of power machine bearings in packaging machinery. |
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