Quantum Computing Assisted Deep Learning for Fault Detection and Diagnosis in Industrial Process Systems
This addresses fault diagnosis in industrial processes, offering a novel hybrid approach but is incremental in combining quantum computing with existing deep learning techniques.
The paper tackles fault detection and diagnosis in industrial process systems by proposing a quantum computing-assisted deep learning method, achieving average fault detection rates of 79.2% for CSTR and 99.39% for the TE process.
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.