Prognostics and health management via quantum machine learning in the oil & gas industry

Abstract

The field of Prognostics and Health Management (PHM) aims to predict the behavior of machines to make informed maintenance decisions. In the Oil and Gas industry, fault mode diagnosis, as a PHM activity, has been applied to rotating machinery such as compressors, centrifugal pumps, and submersible motors using traditional Machine Learning (ML) and Deep Learning techniques. With the emergence of a new and rapidly growing research field called Quantum Computing (QC), there is now potential for even more efficient and accurate predictions. The QC has contributed to different purposes and contexts, such as optimization, artificial intelligence, simulation, cybersecurity, pharmaceutics, and the energy sector. Despite the current limitations of hardware, QC has been explored to improve the speed and efficiency of ML models. This master thesis focuses on the application of Quantum Machine Learning (QML) to diagnose rolling bearings which are essential components in rotating machinery, based on vibration signals. We apply hybrid models involving the encoding and construction of parameterized quantum circuits connected to a classical neural network, the Multi-Layer Perceptron (MLP). The study uses the Variational Quantum Eigensolver framework along with rotation gates and different entanglement (two-qubits) gates (CNOT, CZ and iSWAP), and explores the impact of varying the number of layers (1, 5 and 10) in the quantum circuit. We use two databases of different complexity levels not previously explored with QML, namely Case Western Reserve University (CWRU) and Jiangnan University (JNU), with 10 and 12 failure modes, respectively. For CWRU and JNU, all QML models presented higher accuracy than the classical MLP. These results suggest that, despite the current limitations of quantum environments, QML models are promising tools to be further investigated in PHM activities in the Oil and Gas industry.