Quantum Computing Assisted Deep Learning for Fault Detection and Diagnosis in Industrial Process Systems
arXiv:2003.00264 · doi:10.1016/j.compchemeng.2020.107119
Abstract
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.
References in corpus (16)
- Ising formulations of many NP problems
- Quantum Annealing in the Transverse Ising Model
- Adiabatic Quantum Computing
- Quantum Data Fitting
- Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming
- Quantum computing for energy systems optimization: Challenges and opportunities
- A case study in programming a quantum annealer for hard operational planning problems
- Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning
- Quantum Computing based Hybrid Solution Strategies for Large-scale Discrete-Continuous Optimization Problems
- Application of Quantum Annealing to Training of Deep Neural Networks
- Thermally assisted adiabatic quantum computation
- Noise resistance of adiabatic quantum computation using random matrix theory
- Improving solutions by embedding larger subproblems in a D-Wave quantum annealer
- Adiabatic Quantum Search in Open Systems
- An efficient quantum algorithm for generative machine learning
- Solving SAT and MaxSAT with a Quantum Annealer: Foundations, Encodings, and Preliminary Results