Fault Diagnosis for Rolling Bearings Based on Fine-Sorted Dispersion Entropy and SVM Optimized with Mutation SCA-PSO.
Fault Diagnosis for Rolling Bearings Based on Fine-Sorted Dispersion Entropy and SVM Optimized with Mutation SCA-PSO.
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基于细排序离散熵和变异SCA-PSO优化SVM的滚动轴承故障诊断
DOI：
10.3390/e21040404
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发表时间：
2019-04-16
期刊：
影响因子：
--
通讯作者：
Chen T
中科院分区：
文献类型：
--
作者：
Fu W;Tan J;Xu Y;Wang K;Chen T
滚动轴承是现代工业中广泛使用的重要部件，关系到设备的生产效率和剩余寿命。一种有效、鲁棒的滚动轴承故障诊断方法可以减少因意外故障造成的停机时间。为此，提出了一种基于离散熵和突变正弦余弦算法和粒子群优化（SCA-PSO）优化支持向量机（SVM）的滚动轴承故障诊断新方法，用于诊断不同尺寸、位置和电机负载的故障。首先采用变分模态分解（VMD）将采集到的不同类型故障的振动信号分解为一组本征模态函数（IMFs），其中分解模态数K由中心频率观测方法确定，从而减弱原始信号的非平稳性。然后，提出改进的细排序色散熵（FSDE）来增强对相邻元素之间关系信息的感知，然后利用它来构造不同故障样本的特征向量。然后，提出了一种结合变异算子、正弦余弦算法和粒子群优化（MSCAPSO）优点的混合优化策略来优化支持向量机模型。然后应用最优支持向量机模型实现对不同故障样本的模式识别。通过多次对比实验验证了该方法的优越性。结果分析表明，该方法具有较好的精度和稳定性，在滚动轴承故障诊断领域具有广阔的应用前景。
Rolling bearings are a vital and widely used component in modern industry, relating to the production efficiency and remaining life of a device. An effective and robust fault diagnosis method for rolling bearings can reduce the downtime caused by unexpected failures. Thus, a novel fault diagnosis method for rolling bearings by fine-sorted dispersion entropy and mutation sine cosine algorithm and particle swarm optimization (SCA-PSO) optimized support vector machine (SVM) is presented to diagnose a fault of various sizes, locations and motor loads. Vibration signals collected from different types of faults are firstly decomposed by variational mode decomposition (VMD) into sets of intrinsic mode functions (IMFs), where the decomposing mode number K is determined by the central frequency observation method, thus, to weaken the non-stationarity of original signals. Later, the improved fine-sorted dispersion entropy (FSDE) is proposed to enhance the perception for relationship information between neighboring elements and then employed to construct the feature vectors of different fault samples. Afterward, a hybrid optimization strategy combining advantages of mutation operator, sine cosine algorithm and particle swarm optimization (MSCAPSO) is proposed to optimize the SVM model. The optimal SVM model is subsequently applied to realize the pattern recognition for different fault samples. The superiority of the proposed method is assessed through multiple contrastive experiments. Result analysis indicates that the proposed method achieves better precision and stability over some relevant methods, whereupon it is promising in the field of fault diagnosis for rolling bearings.
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