样本重构多尺度孪生卷积网络的化工过程故障检测

Chemical industrial process fault detection based on sample reconstruction multi-scale siamese CNN

基于数据驱动的故障检测已成为工业过程故障检测的重要手段,但其在实际应用时受限于过程历史数据的规模,往往难以取得令人满意的检测精度。针对这一问题,提出了一种样本空间重构策略,该策略基于随机采样构造同类、异类样本对,在扩充数据规模的同时,将复杂的分类建模问题转化为样本间的相似度对比问题,降低了任务的复杂度。在此基础上,引入并改进孪生卷积神经网络(Siamese CNN)结构,提出了一种基于多尺度孪生卷积神经网络(Multi-scale Siamese CNN)的化工过程故障检测方法。田纳西-伊斯曼(TE)过程数据测试结果表明,所提算法的平均故障检测准确率达到89.66%,相对于常规数据驱动的故障检测算法提高8%以上。

Data-driven based fault detection method has become important means for the fault detection of practical industrial processes,however,in practical application it is often limited by the size of process historical data,so that it is difficult to achieve satisfactory fault detection accuracy.In this paper,aiming at this problem a sample space reconstruction strategy is proposed,which constructs the sample pairs of the same or different categories based on random sampling.While the data size is expanded,the strategy transforms complex classification modeling problem into the comparison problem of the similarity among the samples,which effectively reduces the complexity of the task and the amount of the data needed for modeling.Based on the reconstruction strategy,the siamese CNN is introduced and improved,a chemical industrial process fault detection method based on Multi-scale Siamese Convolutional Neural Networks(Multi-scale Siamese CNN)is proposed.The test results on the Tennessee-Eastman(TE)process dataset verify the effectiveness of the proposed algorithm.The test results show that the average fault detection accuracy of the proposed algorithm reaches 89.66%,which is improved by 8%above compared with that of conventional data-driven fault detection algorithm.