Prognostics and Health Management of Rotating Machinery via Quantum Machine Learning

Prognostics and Health Management (PHM) concerns predicting machines' behavior to support maintenance decisions through failure modes diagnosis and prognosis. Diagnosis is broadly applied in the context of rotating machines' state classification using several traditional Machine Learning (ML) and Deep Learning (DL) methods. Recently, Quantum Computing (QC), a new and expanding research field, has contributed to different purposes and contexts, such as optimization, artificial intelligence, simulation, cybersecurity, pharmaceutics, and the energy sector. Despite the current limitations in terms of hardware, QC has been studied as an alternative for improving models' speed and computational efficiency. Specifically, this paper proposes a Quantum Machine Learning (QML) approach 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 (PQC) connected to a classical neural network, the Multi-Layer Perceptron (MLP). We consider combinations of the Variational Quantum Eigensolver (VQE) framework with rotation gates and different entanglement (two-qubits) gates (CNOT, CZ and iSWAP). For each PQC configuration, we assess the impact of the number of layers (1, 5 and 10). We use two databases of different complexity levels not previously explored with QML, namely CWRU and JNU, with 10 and 12 failure modes, respectivel. For CWRU, all QML models presented higher accuracy than the classical MLP. For JNU, all QML models were superior to classical MLP as well. These results suggest that, despite the current limitations of quantum environments, QML models are promising tools to be further investigated in PHM.