Machine Failure Detection Based on Projected Quantum Models
This work addresses the problem of timely machine failure detection for industrial maintenance, offering a novel quantum approach that is incremental in applying quantum computing to this domain.
This paper tackled machine failure detection by introducing a quantum computing-based algorithm using projected quantum feature maps and change-point detection, which was validated on benchmark and real-world IoT datasets and executed on IBM's 133-qubit Heron processor, demonstrating accurate anomaly identification in noisy time series data.
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.