| 李国友*,才士文*,李东朔**,张新魁*,贾曜宇*,宁泽*.基于MPA优化MKL-FSVDD模型的聚合釜设备故障诊断[J].高技术通讯(中文),2022,32(4):379~391 |
| 基于MPA优化MKL-FSVDD模型的聚合釜设备故障诊断 |
| Polymerization kettle equipment fault diagnosis based on MPA optimized MKL-FSVDD model |
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| DOI： |
| 中文关键词: 故障诊断； 海洋捕食者算法(MPA)； 多核学习(MKL)； 模糊隶属度； 聚合釜 |
| 英文关键词: fault diagnosis, marine predators algorithm (MPA), multiple kernel learning (MKL), fuzzy membership degree, polymerization kettle |
| 基金项目: |
| 作者 | 单位 | | 李国友* | | | 才士文* | | | 李东朔** | | | 张新魁* | | | 贾曜宇* | | | 宁泽* | |
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| 中文摘要: |
| 针对化工流程工业数据具有强非线性、易受噪声影响和故障为多分类的问题，提出一种基于海洋捕食者算法(MPA)优化多核学习-模糊支持向量机数据描述(MKL-FSVDD)的故障诊断方法。利用MKL构建的多核函数，弥补单核函数的局限性，对非线性故障数据分类具有较强的适应性；引入MPA对MKL-FSVDD模型的核参数进行高效寻优，解决核参数选择难题。通过在TE数据平台上的对照实验，验证MPA-MKL-FSVDD模型故障诊断的有效性能；最后将故障诊断模型应用于聚氯乙烯(PVC)聚合反应中，利用70m3的聚合釜设备历史数据集进行仿真验证。结果表明该方法充分利用复杂样本集的数据信息，并在参数寻优阶段快速、稳定获得最优解，保证了故障分类的效率和准确度。 |
| 英文摘要: |
| In order to solve the problems that the data of chemical process industry has strong non-linearity, easy to be affected by noise and the fault is multi-fault classification, a fault diagnosis method based on marine predators algorithm (MPA) optimized multi-kernel learning and fuzzy support vector machine data description (MKL-FSVDD) is proposed. The multi-kernel function constructed by MKL makes up for the limitation of single kernel function, and has strong adaptability to nonlinear fault data classification. MPA is introduced to optimize the kernel parameters of MKL-FSVDD model efficiently, and the problem of kernel parameter selection can be solved. The effective performance of MPA-MKL-FSVDD model for fault diagnosis is verified through the control experiment on the TE data platform. Finally, the algorithm is applied to the polyvinyl chloride (PVC) polymerization reaction, and the historical data set of the 70m3 polymerization kettle is used for simulation verification. The results show that the proposed method makes full use of the data information of the complex sample set, and the optimal solution can be obtained quickly and stably in the parameter optimization stage, which guarantes the efficiency and accuracy of fault classification. |
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