Quantum Kernel Functions for the Prognosis and Health Management of Ball-Bearing Elements

This paper showcases the application of quantum-based kernel methods in Prognosis and Health Management tasks for rotating machinery. We provide a concise yet complete introduction to quantum computing from a probabilistic perspective, describing how a quantum algorithm can be interpreted as a kernel function. Using this framework, we assess the efficacy of quantum kernels in two relevant machine learning domains: the categorization of unlabeled data through the application of a quantum-based clustering algorithm, and the reduction of high dimensional data through quantum-based kernel principal component analysis. These approaches are evaluated using a well-known benchmark dataset of ball-bearing elements. The dataset, originally in the form of time series data collected from accelerometer sensors, consists of three distinct classes, and is first processed to extract pertinent time features. Results indicate that the proposed quantum-based approach performs comparably to traditional methodologies, motivating future research efforts in the use of Quantum Kernels Functions for machine learning applications in the field of Prognosis and Health Management.