Rotating Machinery Health State Diagnosis Through Quantum Machine Learning

Academia and enterprises have explored Prognostic and Health Management (PHM) to perform the diagnosis of failure modes via several traditional Machine and Deep Learning methods. However, the computation scenario is heading toward new advances, which include Quantum Processing Units. Due to its promising results in terms of speed and scalability, research centers worldwide began experimenting with models that lay at the intersection of machine learning and quantum computing. In this sense, a new technique that has already been applied in different scenarios is Quantum Machine Learning (QML), which aims to improve conventional methods in terms of performance and results. This work aims to apply QML models for the fault diagnosis of bearings, an important rotating machinery component, by vibration signals. We apply hybrid models involving the encoding and construction of parameterized quantum circuits connected to a classical neural network. The study uses rotation gates and different entanglement gates (CNOT, CZ and iSWAP), and explores the impact of varying the number of the quantum circuits layers. We perform a classical Multilayer Perceptron model for comparisons purposes. We use the database Case Western Reserve University with 10 failure modes. The obtained results suggest that, despite the current limitations of quantum environments, QML models are promising tools to be further investigated in PHM activities.

Keywords: Quantum machine learning, Rotating machinery, Health state diagnosis, Variational quantum algorithm.

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