Multiple Fault Diagnosis in Distillation Column Using Multikernel Support Vector Machine,Industrial & Engineering Chemistry Research

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Multiple Fault Diagnosis in Distillation Column Using Multikernel Support Vector Machine
Industrial & Engineering Chemistry Research ( IF 3.8 ) Pub Date : 2018-10-17 , DOI: 10.1021/acs.iecr.8b03360
Syed A. Taqvi _{1,

2} , Lemma Dendena Tufa ₁ , Haslinda Zabiri ₁ , Abdulhalim Shah Maulud ₁ , Fahim Uddin _{1,

2}

Affiliation

Fault detection and diagnosis (FDD) in process industries is an important task for efficient process monitoring and plant safety. It is also essential for improving product quality and reducing production cost by reducing process downtime. Real-time multiscale classification of faults plays a vital role in process monitoring. However, some major issues such as high correlation, complexity, and nonlinearity of data are yet to be addressed. In this paper, a fault diagnosis approach based on multikernel support vector machines is proposed to classify the internal and external faults such as reflux failure, change in reboiler duty, column tray upsets, and change in feed composition, flow, and temperature in a distillation column. The data set is generated using Aspen plus dynamics simulation at normal and faulty states. The classification has been done by various methods such as decision tree, K-nearest neighbors, linear discriminant analysis, artificial neural network, subspace discriminant, and multikernel support vector machine. It is observed that the SVM based diagnostic system provides more accurate root cause isolation. The proposed MK-SVM method was evaluated by using the confusion matrix as the performance evaluator. The result showed that the proposed model has a high FDR which is 99.77% and a very low FAR, i.e., 0.23%.

中文翻译：

基于多核支持向量机的精馏塔多重故障诊断。

流程工业中的故障检测和诊断（FDD）是有效进行流程监控和工厂安全的重要任务。对于通过减少过程停机时间来提高产品质量和降低生产成本而言，这也是必不可少的。故障的实时多尺度分类在过程监控中起着至关重要的作用。但是，诸如数据的高相关性，复杂性和非线性之类的一些主要问题尚待解决。在本文中，提出了一种基于多核支持向量机的故障诊断方法，以对内部和外部故障进行分类，例如回流故障，再沸器负荷变化，塔板塔架不稳以及蒸馏过程中进料组成，流量和温度的变化。柱子。数据集是在正常和故障状态下使用Aspen plus动力学模拟生成的。分类已通过各种方法完成，例如决策树，K近邻，线性判别分析，人工神经网络，子空间判别和多核支持向量机。可以看出，基于SVM的诊断系统可提供更准确的根本原因隔离。通过使用混淆矩阵作为性能评估器来评估所提出的MK-SVM方法。结果表明，所提出的模型具有高的FDR，为99.77％和非常低的FAR，即为0.23％。通过使用混淆矩阵作为性能评估器来评估所提出的MK-SVM方法。结果表明，所提出的模型具有高的FDR，为99.77％和非常低的FAR，即为0.23％。通过使用混淆矩阵作为性能评估器来评估所提出的MK-SVM方法。结果表明，所提出的模型具有高的FDR，为99.77％和非常低的FAR，即为0.23％。

更新日期：2018-10-17

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