Multiclass classification of big data is a subject of broad interest in machine learning research nowadays, where it is necessary to extract important features from a dataset’s variables in order to accurately detect unique variations between different classes of data. In this paper, we will discuss the application of a novel combination of different fault detection-based techniques towards the problem of multiclass classification of different types of faults found in the benchmark Tennessee Eastman Process. Moreover, the fault detection performance and data classification accuracy of our proposed method is compared to the respective performances of multiple data-driven methods tabulated in literature, including deep neural networks. The results show that a combined application of multiple fault detection techniques, in tandem with the one-versus-all and all-versus-all binary decomposition methods, can provide a competitive multiclass classification accuracy, comparative to other more complex methods in literature.

如今，大数据的多类分类是机器学习研究中广泛关注的主题，在这种情况下，有必要从数据集的变量中提取重要特征，以便准确检测不同数据类别之间的独特变化。在本文中，我们将讨论基于故障检测的不同技术的新颖组合在基准田纳西伊士曼过程中发现的不同类型故障的多分类问题中的应用。此外，将我们提出的方法的故障检测性能和数据分类精度与文献中包括深度神经网络的多种数据驱动方法的各自性能进行了比较。结果表明，多种故障检测技术的组合应用，