用量子特征映射提升设备故障检测精度，实现在真实传感器数据上的有效应用。
Machine Failure Detection Based on Projected Quantum Models
- 结合量子特征映射与统计变点检测，增强异常识别能力。
- 在133量子比特处理器上运行，成功识别噪声时间序列中的故障信号。
- 适合关注量子计算工业落地、预测性维护的科研与工程人员。
及时检测设备故障对工业领域维持效率、减少停机至关重要。本文提出一种基于量子计算与统计变点检测的故障检测算法，利用投影量子特征映射提升机器监控系统中异常检测的精度。我们在基准多维时间序列数据集及包含实际物联网传感器读数的真实数据集上验证了该方法，确保研究的实际意义。算法在IBM 133量子比特Heron处理器上执行，证明了量子计算在工业维护流程中集成的可行性。实验结果表明，该量子基故障检测系统能有效识别噪声时间序列中的异常，凸显其在工业诊断中的潜力，并为预测性维护领域更复杂量子算法的发展铺平道路。
原文摘要 · Abstract (English)
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.
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