Machine Failure Detection Based on Projected Quantum Models

TL;DR

This paper presents a novel quantum computing-based failure detection algorithm that uses projected quantum feature maps and change-point detection to identify anomalies in industrial machine data, validated on real and benchmark datasets.

Contribution

It introduces a new quantum-enhanced failure detection method utilizing projected quantum feature maps and demonstrates its practical implementation on a 133-qubit quantum processor.

Findings

Effective anomaly detection in noisy time series data

Successful implementation on IBM's quantum hardware

Improved precision over classical methods

Abstract

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.