2021年12月27日下午4点30分，我院在教学科研楼507室邀请博士研究生徐定鑫同学做线下学术报告，2021级全体硕士研究生参加了此次会议。

报告题目：Deep Forest-Based Fault Diagnosis Method for Chemical Process

摘要：With the rapid expanding of big data in all domains, data-driven and deep learning-based fault diagnosis methods in chemical industry have become a major research topic in recent years. In addition to a deep neural network, deep forest also provides a new idea for deep representation learning and overcomes the shortcomings of a deep neural network such as strong parameter dependence and large training cost. However, the ability of each base classififier is not taken into account in the standard cascade forest, which may lead to its indistinct discrimination. In this paper, a multigrained scanning-based weighted cascade forest (WCForest) is proposed and has been applied to fault diagnosis in chemical processes. In view of the high-dimensional nonlinear data in the process of chemical industry, WCForest fifirst designs a set of relatively suitable windows for the multigrained scan strategy to learn its data representation. Next, considering the fifitting quality of each forest classififier, a weighting strategy is proposed to calculate the weight of each forest in the cascade structure without additional calculation cost, so as to improve the overall performance of the model. In order to prove the effffectiveness of WCForest, its application has been carried out in the benchmark Tennessee Eastman (TE) process. Experiments demonstrate that WCForest achieves better results than other related approaches across various evaluation metrics.

会议过程中徐定鑫同学首先简要阐述了其论文的研究背景，表明最大限度的利用海量数据去进一步提高故障诊断的性能显得尤为重要。其次介绍了在故障诊断中用到的一些统计学方法：主成分分析、偏最小二乘、独立成分分析、Fisher判别分析、随机森立、卷积相关分析。最后就本文提出的一种基于多粒度扫描的加权级联森林（WCForest），并将其应用于化工过程的故障诊断，向大家进行了细致而透彻的讲解。通过改进的深森林模型WCForest，用于化工过程故障诊断，以提高诊断准确率，降低误报率，处理高维和非线性数据。主要表现是，在不增加计算复杂性的情况下,k折交叉验证用于计算的重量级联结构中的每个森林以提高级联随机森林的良好的性能，削弱不良性能，从而提高级联随机森林整体性能。结果表明，该方法能够有效地预测TE过程的故障诊断，为其他化工过程的故障诊断提供参考。

报告结束后，通过徐定鑫同学对其成果的详细讲解，同学们对深森林的化工过程故障诊断方法有了更加系统深入的认识。本次学术交流会通过大家面对面的交流，拓宽了大家的学术视野，激发了钻研学术的热情，尤其是疫情期间的科研，更能够增强作为研究生的使命感和责任感。

数学与统计学院

2021年12月27日