Quantum Computing Assisted Deep Learning for Fault Detection and Diagnosis in Industrial Process Systems

  title={Quantum Computing Assisted Deep Learning for Fault Detection and Diagnosis in Industrial Process Systems},
  author={Akshay Ajagekar and Fengqi You},
  journal={Comput. Chem. Eng.},

Quantum Computing: Fundamentals, Trends and Perspectives for Chemical and Biochemical Engineers

This article reviews and summarizes the state of the art to gain insight into how quantum computation can benefit and optimize chemical engineering issues and offers an outlook of future directions within the field.

Bearing fault diagnosis based on Gramian angular field and DenseNet

The transfer learning (TL), which can solve the problem of insufficient samples, is integrated to the DenseNet to enhance its extensibility and the comparative simulations are carried out to illustrate the effectiveness of the proposed method.

An automatic and intelligent brain tumor detection using Lee sigma filtered histogram segmentation model

Qualitative and quantitative results illustrate that the proposed Lee sigma filtered histogram segmentation (LSFHS) technique attains greater performance than state-of-the-art methods on tumor detection accuracy and less time.

Deep Reinforcement Learning Based Automatic Control in Semi-Closed Greenhouse Systems

This work proposes a novel deep reinforcement learning (DRL) based control framework for greenhouse climate control that utilizes a neural network to approximate state-action value estimation and results indicate that the proposed Q-learning based DRL framework yields higher cumulative returns.

Semiconductor Defect Detection by Hybrid Classical-Quantum Deep Learning

  • Yuanfu YangMin Sun
  • Computer Science
    2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2022
A classical-quantum hybrid algorithm for deep learning on near-term quantum processors is proposed and quantum circuit driven by this framework learns a given DLDR task, include of wafer defect map classification, defect pattern classification, and hotspot detection.

Feature Ensemble Net: A Deep Framework for Detecting Incipient Faults in Dynamical Processes

A novel feature ensemble net (FENet) was developed, particularly for faults 3, 9, and 15 in the Tennessee Eastman process (TEP), which are notoriously difficult to detect.



A deep belief network based fault diagnosis model for complex chemical processes

A Convolutional Neural Network for Fault Classification and Diagnosis in Semiconductor Manufacturing Processes

A convolutional neural network model, named FDC-CNN, is proposed, in which a receptive field tailored to multivariate sensor signals slides along the time axis, to extract fault features, making it possible to locate the variable and time information that represents process faults.

Fault diagnosis based on deep learning

The proposed method not only improves the divisibility between faults and normal process, but also exhibits a better performance on the accuracy of fault classification for the chemical benchmark, Tennessee Eastman Process (TEP) data.

Deep convolutional neural network model based chemical process fault diagnosis

An efficient quantum algorithm for generative machine learning

This work proposes an efficient quantum algorithm for machine learning based on a quantum generative model that is exponentially more powerful to represent probability distributions compared with classical generative models and has exponential speedup in training and inference at least for some instances under a reasonable assumption in computational complexity theory.

Application of Quantum Annealing to Training of Deep Neural Networks

This work investigated an alternative approach that estimates model expectations of Restricted Boltzmann Machines using samples from a D-Wave quantum annealing machine, and found that the quantum sampling- based training approach achieves comparable or better accuracy with significantly fewer iterations of generative training than conventional CD-based training.

Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning

A systematic study assessing the impact of the effective temperatures in the learning of a special class of a restricted Boltzmann machine embedded on quantum hardware, which can serve as a building block for deep-learning architectures.

A neural network methodology for process fault diagnosis

A neural-network-based methodology for providing a potential solution to the preceding problems in the area of process fault diagnosis is proposed and compared with the knowledge-based approach.