A Quantum Q-Learning Fault Diagnosis Method for Intelligent Manufacturing Equipment

Abstract

In the era of rapid industrial automation advancements, the complexity of intelligent manufacturing equipment has been steadily escalated. Stringent demands for high-efficiency and high-precision diagnosis are increasingly being unmet by conventional fault diagnosis methods. To address these challenges, a novel fault diagnosis approach grounded in quantum Q-learning is presented in this paper. The distinct advantages of quantum computing are innovatively integrated with the decision-making framework of Q-learning through this method. By harnessing the multi-information-carrying capacities of qubits, vast amounts of multi-source heterogeneous data generated during equipment operation can be efficiently processed. Latent fault features are thereby rapidly uncovered, significantly reducing the time required for fault-feature extraction. Furthermore, optimal decisions can be dynamically formulated by Q-learning within evolving production environments, leveraging precise analysis outcomes from quantum computing. Real-time equipment status is continuously monitored to accurately identify fault types, pinpoint locations, and promptly generate targeted maintenance strategies. Fault-diagnosis tests conducted on typical industrial intelligent manufacturing equipment demonstrate that the quantum Q-learning method outperforms traditional approaches in terms of diagnosis accuracy, efficiency, and adaptability to complex fault patterns. This breakthrough opens up new frontiers for fault diagnosis in intelligent manufacturing systems.