An Imbalance Modified Deep Neural Network With Dynamical Incremental Learning for Chemical Fault Diagnosis
An Imbalance Modified Deep Neural Network With Dynamical Incremental Learning for Chemical Fault Diagnosis
复制标题
用于化学故障诊断的具有动态增量学习的不平衡改进深度神经网络
DOI：
10.1109/tie.2018.2798633
复制
发表时间：
2019
影响因子：
7.7
通讯作者：
Peng Jiang
中科院分区：
文献类型：
--
作者：
Zhixin Hu;Peng Jiang
在本文中，研究了处理化学不平衡数据流的数据驱动的故障诊断模型。由于不断到达化工厂，不同的故障以不同的频率发生，而这个问题在开发诊断模型时很难得到解决。提出了一种新颖的增量不平衡改进深度神经网络（增量IMDNN）来促进对不平衡数据流的故障诊断。设计增量 IMDNN 的第一步是采用不平衡修正方法与主动学习相结合，以提取和生成最有价值的信息，同时考虑模型反馈。利用DNN作为基本诊断模型来挖掘潜在信息。然后针对新故障模式的不断到来，以增量分层的方式提升DNN。与基于静态数据快照训练的传统模型不同，该模型继承了现有知识，并通过故障的相似性分层扩展诊断模型。通过模糊聚类判断的相似故障合并为一个超类，每个子模型共享先前研究中普遍存在的相同架构，可以并行训练。我们在田纳西州伊士曼（TE）数据集中验证了所提出方法的性能，模拟结果表明所提出的增量IM-DNN优于现有方法，并且在化学故障诊断中具有显着的鲁棒性和适应性。
In this paper, a data-driven fault diagnosis model dealing with chemical imbalanced data streams is investigated. Different faults occur with varied frequencies by continuous arrival in chemical plants, while this issue has been hardly addressed in developing a diagnosis model. A novel incremental imbalance modified deep neural network (incremental-IMDNN) is proposed to promote the fault diagnosis to the imbalanced data stream. The first step in designing the incremental-IMDNN is the employment of an imbalance modified method combined with active learning for the extraction and generation of the most valuable information keeping in view the model feedback. DNN is utilized as a basic diagnosis model to excavate potential information. Then for the continuous arrival of new fault modes, DNN is promoted in an incremental hierarchical way. Unlike the traditional model that trained on a static snapshot of data, this model inherits the existing knowledge and hierarchically expands the diagnosis model by the similarity of faults. Similar faults that are judged by fuzzy clustering merge into a superclass, and every submodel shares the same architecture that is prevalent in previous research, which can be trained in parallel. We validate the performance of the proposed method in a Tennessee Eastman (TE) dataset, and the simulation results indicate that the proposed incremental-IM-DNN is better than the existing methods and possesses significant robustness and adaptability in chemical fault diagnosis.
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