Bearing Fault Diagnosis using Quantum Machine Learning

Bearing fault diagnostics is essential for ensuring operational effectiveness and preventing failures across various industries, which often result in substantial financial losses. The development of quantum machine learning has introduced a paradigm shift in defect diagnostic methods, leveraging the computational power of quantum computers. This study investigates the impact of quantum computing and classical computing algorithms on Case Western Reserve University bearing dataset. The proposed methodology encompasses normalization, segmentation, data augmentation, and feature extraction/selection, executed on classical computer. Selected classical features are encoded as quantum state amplitudes, followed by the application of superposition and entanglement to qubits. The classification stage employs a quantum support vector machine, implemented, and simulated using the available IBM quantum resources. The results showcase 99.61% training accuracy and 99.14% testing accuracy with overall F1-score, recall, and precision of 99%. Comparative analysis between classical and quantum machine learning algorithms is conducted perfomace anlysis. These outcomes underscore the potential of quantum machine learning in defect diagnosis, offering heightened precision and reliability in practical applications with fast computing.