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Sintering conditions recognition of rotary kiln based on kernel modification considering class imbalance.
ISA Transactions ( IF 7.3 ) Pub Date : 2020-07-09 , DOI: 10.1016/j.isatra.2020.07.010
Dingxiang Wang 1 , Xiaogang Zhang 1 , Hua Chen 2 , Yicong Zhou 3 , Fanyong Cheng 4
Affiliation  

Accurate sintering condition recognition (SCR) is an important precondition for optimal control of rotary kilns. However, the occurrence probability of abnormal conditions in the industrial field is much lower than normal, resulting in imbalanced class sintering samples in general. This significantly deteriorates the effectiveness of existing recognition models in abnormal condition detection. In this paper, an integrated framework considering class imbalance is proposed for sintering condition recognition. In the proposed framework, after analysing the characteristics of thermal signals by the Lipschitz method, four discriminant features are extracted to comprehensively describe different sintering conditions. In addition, focusing on the class imbalance of sintering samples, the kernel modification method is introduced to enhance the optimal marginal distribution machine (ODM), and a novel recognition model kernel modified the ODM (KMODM) is proposed for SCR. By constructing a new conformal transformation function to modify the ODM kernel function, KMODM optimizes the spatial distribution of training samples in the kernel space, thereby alleviating the detection accuracy deterioration of the minority class. The experimental results on real thermal signals and standard datasets show that the KMODM model can effectively handle imbalanced data. Based on this, the proposed SCR framework can reduce the misjudgement of abnormal conditions and balance the recognition accuracy of each condition.



中文翻译:

考虑类不平衡的基于核修正的回转窑烧结条件识别。

准确的烧结条件识别(SCR)是优化控制回转窑的重要前提。但是,工业领域中异常条件的发生概率比正常情况低得多,通常导致等级烧结样品不平衡。这大大恶化了现有识别模型在异常状况检测中的有效性。本文提出了一种考虑类不平衡的综合框架,用于烧结条件识别。在提出的框架中,通过Lipschitz方法分析了热信号的特征,提取了四个判别特征以全面描述不同的烧结条件。此外,重点关注烧结样品的类别不平衡,引入了核修正方法以增强最优边际分配机(ODM),提出了一种新的基于SCR的ODM识别模型核(KMODM)。通过构造新的保形变换函数来修改ODM核函数,KMODM优化了训练样本在核空间中的空间分布,从而减轻了少数类检测精度的下降。对实际热信号和标准数据集的实验结果表明,KMODM模型可以有效处理不平衡数据。基于此,本文提出的SCR框架可以减少对异常情况的误判,并可以平衡每种情况的识别精度。通过构造新的保形变换函数来修改ODM核函数,KMODM优化了训练样本在核空间中的空间分布,从而减轻了少数类检测精度的下降。对实际热信号和标准数据集的实验结果表明,KMODM模型可以有效处理不平衡数据。基于此,本文提出的SCR框架可以减少对异常情况的误判,并可以平衡每种情况的识别精度。通过构造新的保形变换函数来修改ODM核函数,KMODM优化了训练样本在核空间中的空间分布,从而减轻了少数类检测精度的下降。对实际热信号和标准数据集的实验结果表明,KMODM模型可以有效处理不平衡数据。基于此,本文提出的SCR框架可以减少对异常情况的误判,并可以平衡每种情况的识别精度。对实际热信号和标准数据集的实验结果表明,KMODM模型可以有效处理不平衡数据。基于此,本文提出的SCR框架可以减少对异常情况的误判,并可以平衡每种情况的识别精度。对实际热信号和标准数据集的实验结果表明,KMODM模型可以有效处理不平衡数据。基于此,本文提出的SCR框架可以减少对异常情况的误判,并可以平衡每种情况的识别精度。

更新日期:2020-07-09
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