Quantum Machine Intelligence

Solving industrial fault diagnosis problems with quantum computers

Alexander Diedrich, Stefan Windmann, Oliver Niggemann

Published Oct 8, 2024• 3 citations

Abstract

Abstract In this article, we investigate in how far quantum computers can be leveraged to solve NP-complete fault diagnosis problems within the area of industrial cyber-physical systems. Therefore, two approaches are proposed which exploit quantum computing to solve diagnosis problems: The first method employs Grover’s algorithm, and the second is based on the Quantum Approximate Optimization Algorithm. To show the industrial application, we present an integrated approach to learn the diagnosis model from process data, check whether the model is suitable, and use it for diagnosis. The result is a method for quantum industrial fault diagnosis. For this approach, the diagnostic capabilities and the runtime have been evaluated on an IBM Falcon processor using three publicly available benchmarks from the process industry. Further, the scaling between quantum computers and classical PCs has been analyzed.

DOI: 10.1007/s42484-024-00184-x

Related papers

A Deep Learning Approach for Fault Detection and Diagnosis of Industrial Processes using Quantum Computing

Akshay Ajagekar, Fengqi You

Quantum computing and deep learning methods hold great promise to open up a new era of computing and have been receiving significant attention recently. This paper presents quantum computing (QC) based deep learning methods for fault diagnosis that are capable of overcoming the computational challenges faced by conventional techniques performed on classical computers. The shortcomings of such classical data-driven techniques are addressed by the proposed QC-based fault diagnosis model. A quantum computing assisted generative training process followed by supervised discriminative training is used to train this model. The applicability of proposed model and methods is demonstrated by applying them to process monitoring of Tennessee Eastman (TE) process. The proposed QC-based deep learning approach enjoys superior performance with an average fault diagnosis rate of 80% and tremendously low false alarm rates for the TE process.

Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and CyberneticsOct 11, 20202 citations

Bearing Fault Diagnosis Using Quantum Machine Learning

Misha Urooj Khan, Muhammad Ahmad Kamran, Wajiha Rahim Khan

Bearing fault diagnostics is essential for ensuring operational effectiveness and preventing failures across various industries, which often result in substantial financial losses. The development of quantum machine learning has introduced a paradigm shift in defect diagnostic methods, leveraging the computational power of quantum computers. This study investigates the impact of quantum computing and classical computing algorithms on Case Western Reserve University bearing dataset. The proposed methodology encompasses normalization, segmentation, data augmentation, and feature extraction/selection, executed on classical computer. Selected classical features are encoded as quantum state amplitudes, followed by the application of superposition and entanglement to qubits. The classification stage employs a quantum support vector machine, implemented, and simulated using the available IBM quantum resources. The results showcase 99.61% training accuracy and 99.14% testing accuracy with overall F1-score, recall, and precision of 99%. Comparative analysis between classical and quantum machine learning algorithms is conducted perfomace anlysis. These outcomes underscore the potential of quantum machine learning in defect diagnosis, offering heightened precision and reliability in practical applications with fast computing.

International Bhurban Conference on Applied Sciences and Technology (IBCAST)Aug 22, 20233 citations

Big Data-Driven Modeling and Optimization of Complex Industrial Systems Using Quantum Computing

P. William, Smita M Nirkhi, Mohammed Almakki +3 more

In the rapidly evolving landscape of Industry 4.0, industrial systems have become increasingly complex, interconnected, and data-intensive. Traditional computational methods often fall short in managing the highdimensional, nonlinear challenges presented by such environments. This paper explores a novel framework that integrates Big Data analytics with quantum computing techniques to model and optimize complex industrial systems-including Smart Grids, Supply Chains, and Manufacturing processes. By leveraging algorithms such as Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), and Quantum Machine Learning (QML), the study demonstrates substantial improvements in processing time, accuracy, and resource efficiency compared to classical methods. Empirical evaluation through a structured experimental design and statistical validation via ANOVA confirms that hybrid quantum-classical approaches outperform traditional models across multiple performance metrics. This integration paves the way for real-time, scalable, and intelligent industrial optimization, offering a transformative leap in operational decision-making, fault prediction, and strategic control.

International Conference on Innovative Practices in Technology and Management (ICIPTM)Feb 19, 20260 citations

Power system fault diagnosis with quantum computing and efficient gate decomposition

Xiang Fei, Huan Qi Zhao, Xiyuan Zhou +3 more

Power system fault diagnosis is crucial for identifying the location and causes of faults and providing decision-making support for power dispatchers. However, most classical methods suffer from significant time-consuming, memory overhead, and computational complexity issues as the scale of the power system concerned increases. With rapid development of quantum computing technology, the combinatorial optimization method based on quantum computing has shown certain advantages in computational time over existing methods. Given this background, this paper proposes a quantum computing based power system fault diagnosis method with the quantum approximate optimization algorithm. The proposed method reformulates the fault diagnosis problem as a Hamiltonian by using Ising model, which completely preserves the coupling relationship between faulty components and various operations of protective relays and circuit breakers. Additionally, to enhance problem-solving efficiency under current equipment limitations, the symmetric equivalent decomposition method of multi-z-rotation gate is utilized. Furthermore, the small probability characteristics of power system events is utilized to reduce the number of qubits. Simulation results based on the test system show that the proposed methods can achieve the same optimal results with a faster speed compared with the classical higher-order solver provided by D-Wave.

arXiv:2401.09800Scientific ReportsJul 23, 202417 citations

Rolling bearing fault diagnosis based on quantum LS-SVM

Yuanyuan Li, Liyuan Song, Qichun Sun +4 more

Abstract Rolling bearing is an indispensable part of the contemporary industrial system, and its working conditions affect the state of the entire industrial system. Therefore, there is great engineering value to researching and improving the fault diagnosis technology of rolling bearings. However, with the involvement of the whole mechanical equipment, we need to have a large quantity of data to support the accuracy of fault diagnosis, while the efficiency of classical machine learning algorithms is poor in processing big data, and huge amount of computing resources is required. To solve this problem, this paper combines the HHL algorithm in quantum computing with the LS-SVM algorithm in machine learning and proposes a fault diagnosis model based on a quantum least square support vector machine (QSVM). Based on experiments simulated on analog quantum computers, we demonstrate that our fault diagnosis based on QSVM is feasible, and it can play a far superior advantage over the classical algorithm in the context of big data.

EPJ Quantum TechnologyJun 30, 202224 citations

Machine Failure Detection Based on Projected Quantum Models

Larry Bowden, Qi Chu, Bernard Cena +4 more

Detecting machine failures promptly is of utmost importance in industry for maintaining efficiency and minimizing downtime. This paper introduces a failure detection algorithm based on quantum computing and a statistical change-point detection approach. Our method leverages the potential of projected quantum feature maps to enhance the precision of anomaly detection in machine monitoring systems. We empirically validate our approach on benchmark multi-dimensional time series datasets as well as on a real-world dataset comprising IoT sensor readings from operational machines, ensuring the practical relevance of our study. The algorithm was executed on IBM's 133-qubit Heron quantum processor, demonstrating the feasibility of integrating quantum computing into industrial maintenance procedures. The presented results underscore the effectiveness of our quantum-based failure detection system, showcasing its capability to accurately identify anomalies in noisy time series data. This work not only highlights the potential of quantum computing in industrial diagnostics but also paves the way for more sophisticated quantum algorithms in the realm of predictive maintenance.

quant-pharXiv:2601.15641Jan 23, 2026