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首页> 外文期刊>Journal of natural gas science and engineering >Simultaneous Fault Diagnosis using multi class support vector machine in a Dew Point process
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Simultaneous Fault Diagnosis using multi class support vector machine in a Dew Point process

机译：在露点过程中使用多类支持向量机同时进行故障诊断

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摘要

There are different approaches for Process Fault Diagnosis (PFD) ranging from analytical to statistical methods, such as artificial intelligence. Support vector machine (SVM) is a relatively novel machine learning method which can be used to handle fault classification due to its good generalization ability. The PFD based on Multi Label SVM approach (MLSVM) overcomes the difficulties of the Mono Label Artificial Neural Network (MLANN) approach including the needs for a large number of data points with difficult data gathering procedure and time consuming computation. However, the existing MLSVM approach has a lower classification performance. In this paper the objective is to improve the diagnosis performance of MLSVM approach while maintaining its advantages. Therefore, a novel MLSVM approach based on multiple regulation parameters is proposed for simultaneous fault classification in a Dew Point process. The performance of the proposed MLSVM approach is compared against other classifiers approaches including MLANN and MLSVM with single regulation parameter tuning. The classification performance of the proposed approach is close to MLANN approach and superior than MLSVM with single regulation parameter. However, MLSVM has other advantages in comparison with the MLANN approach including requirement of smaller number of data, easy data gathering and lower computational burden. (C) 2015 Elsevier B.V. All rights reserved.
机译：流程故障诊断（PFD）有多种方法，从分析方法到统计方法，例如人工智能。支持向量机（SVM）是一种相对新颖的机器学习方法，由于其良好的泛化能力，可用于处理故障分类。基于多标签支持向量机方法（MLSVM）的PFD克服了单标签人工神经网络（MLANN）方法的困难，包括对大量数据点的需求，这些数据点具有困难的数据收集过程和耗时的计算。但是，现有的MLSVM方法的分类性能较低。本文的目的是在保持其优势的同时提高MLSVM方法的诊断性能。因此，针对露点过程中的同时故障分类，提出了一种基于多个调节参数的新型MLSVM方法。将所提出的MLSVM方法的性能与其他分类器方法（包括具有单调节参数调整的MLANN和MLSVM）进行比较。提出的方法的分类性能接近于MLANN方法，并且优于具有单一调节参数的MLSVM。但是，与MLANN方法相比，MLSVM具有其他优点，包括需要较少数量的数据，易于收集数据和较低的计算负担。 （C）2015 Elsevier B.V.保留所有权利。

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