论文标题
量子计算有助于深度学习，以实现工业过程系统中的故障检测和诊断
Quantum Computing Assisted Deep Learning for Fault Detection and Diagnosis in Industrial Process Systems
论文作者
论文摘要
近年来，量子计算（QC）和深度学习技术引起了广泛的关注。本文提出了基于质量控制的深度学习方法，以利用其独特的功能来克服经典计算机上传统数据驱动方法所面临的计算挑战。深信网络被整合到建议的故障诊断模型中，用于提取不同级别的特征，以实现正常和错误的过程操作。基于QC的故障诊断模型使用量子计算辅助生成训练过程，然后进行判别训练来解决经典算法的缺点。为了证明其适用性和效率，提出的故障诊断方法用于连续搅拌坦克反应堆（CSTR）和田纳西·伊士曼（TE）工艺的过程监测。提出的基于质量控制的深度学习方法享有卓越的故障检测和诊断性能，而CSTR和TE过程的平均故障检测率分别为79.2％和99.39％。
Quantum computing (QC) and deep learning techniques have attracted widespread attention in the recent years. This paper proposes QC-based deep learning methods for fault diagnosis that exploit their unique capabilities to overcome the computational challenges faced by conventional data-driven approaches performed on classical computers. Deep belief networks are integrated into the proposed fault diagnosis model and are used to extract features at different levels for normal and faulty process operations. The QC-based fault diagnosis model uses a quantum computing assisted generative training process followed by discriminative training to address the shortcomings of classical algorithms. To demonstrate its applicability and efficiency, the proposed fault diagnosis method is applied to process monitoring of continuous stirred tank reactor (CSTR) and Tennessee Eastman (TE) process. The proposed QC-based deep learning approach enjoys superior fault detection and diagnosis performance with obtained average fault detection rates of 79.2% and 99.39% for CSTR and TE process, respectively.
