Variational masking progressive learning method for multi-rate industrial processes soft sensor

IF 3.3 2区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Xuan Hu , Peihao Zheng , Hao Wu , Yongming Han , Zhiqiang Geng
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引用次数: 0

Abstract

Deep learning has been widely used in industrial processes to predict critical quality indicators. However, existing methods assume that industrial process data are uniformly sampled, which is far from real industrial scenarios. To solve the problem of multi-rate sampling in industrial processes, a variational masking progressive learning (VMPL) method is proposed for multi-rate industrial processes soft sensor. In the VMPL, a multi-rate decomposition strategy (MDS) is first developed to construct generalized multi-rate data and corresponding masking matrix. Then, based on the MDS, a variational masking network (VMN) is designed to represent the uncertain distribution information of industrial process data. Meanwhile, the progressive learning (PL) algorithm is derived to assist the VMN in transferring process features from high-rate to low-rate data. Therefore, the VMPL can progressively mine features in different rates data without changing the structure of the VMN to improve soft-sensing accuracy. Finally, compared with the state-of-the-art multi-rate soft sensor model on the three key quality variable datasets of the catalytic cracking process, the VMPL achieves more accurate soft sensing results.
多速率工业过程软传感器的变分掩蔽渐进式学习方法
深度学习已广泛应用于工业过程中预测关键质量指标。然而,现有的方法假设工业过程数据是均匀采样的,这与真实的工业场景相去甚远。为了解决工业过程中多速率采样的问题,提出了一种多速率工业过程软传感器的变分掩蔽渐进式学习方法。在VMPL中,首先提出了一种多速率分解策略(MDS)来构造广义多速率数据和相应的掩蔽矩阵。然后,在此基础上,设计了一个变分掩蔽网络(VMN)来表示工业过程数据的不确定分布信息。同时,推导了渐进式学习(PL)算法,以帮助VMN将过程特征从高速率数据传输到低速率数据。因此,在不改变VMN结构的情况下,VMPL可以在不同速率数据中逐步挖掘特征,从而提高软测量精度。最后,在催化裂化过程的三个关键质量变量数据集上,与目前最先进的多速率软测量模型相比,VMPL获得了更精确的软测量结果。
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来源期刊
Journal of Process Control
Journal of Process Control 工程技术-工程:化工
CiteScore
7.00
自引率
11.90%
发文量
159
审稿时长
74 days
期刊介绍: This international journal covers the application of control theory, operations research, computer science and engineering principles to the solution of process control problems. In addition to the traditional chemical processing and manufacturing applications, the scope of process control problems involves a wide range of applications that includes energy processes, nano-technology, systems biology, bio-medical engineering, pharmaceutical processing technology, energy storage and conversion, smart grid, and data analytics among others. Papers on the theory in these areas will also be accepted provided the theoretical contribution is aimed at the application and the development of process control techniques. Topics covered include: • Control applications• Process monitoring• Plant-wide control• Process control systems• Control techniques and algorithms• Process modelling and simulation• Design methods Advanced design methods exclude well established and widely studied traditional design techniques such as PID tuning and its many variants. Applications in fields such as control of automotive engines, machinery and robotics are not deemed suitable unless a clear motivation for the relevance to process control is provided.
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