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.
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.
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.
The study conducted by the prognostics and health management (PHM) entity inquires into the prediction of machine behavior to aid in decision-making through failure modes classification via diagnostics and estimation. This is primarily performed as a classification of the state of rotating machines using diversified The methodologies of Machine Learning (ML) and Deep Learning (DL) have garnered significant attention in contemporary research. In recent times, Quantum Computing (QC) has surfaced as a novel domain of inquiry. With applications in various fields such as optimization, artificial intelligence (AI), gaming, cybersecurity, pharmaceuticals, and the energy sector. Despite hardware limitations, QC is considered an approach to accelerate models and enhance computational power. This paper presents a QML approach for detecting defects in rolling element bearings of rotating machinery using vibration signals. The method employs a hybrid model: a classical neural network to represent a class of parameterized quantum circuits (PQCs), specifically the multi-layer perceptron (MLP). Under different configurations of the Variational Quantum Eigensolver (VQE) system, various types of spin gates and unique two-qubit gates such as CNOT, CZ, and ISWAP are implemented in each PQC configuration. The performance of these PQC configurations is evaluated using two databases of varying complexities: JNU with a failure mode of 10, and CWRRU with a failure mode of 12. Performance results show that all QML models outperform the classical MLP for the two databases considered. This indicates the promising potential of QML models in the field of PHM, despite the current fragile quantum environments.
In Production Engineering, there is an area of research on reliability, maintenance, and risks in Production Systems. In this area, the objectives include maintenance policies aimed at predicting component failures and consequently minimizing unexpected downtimes of complex systems. Reliability engineering has successfully utilized machine learning to predict and categorize equipment and machine states. In this thesis, we aim to compare different machine learning algorithms and quantum machine learning in the context of reliability engineering. Specifically, we compare 8 models created with quantum machine learning, using three different bearing datasets: Case Western Reserve University, Machinery Failure Prevention Technology, and Paderborn University. These datasets mainly consist of accelerometer vibration data from the bearings, which must be processed to fit the different quantum machine learning algorithms. We focus on fault detection in rotating equipment, using quantum computing techniques to analyze sensor data installed on the equipment. Our approach considers the healthy state, inner ring faults, and outer ring faults of the bearings, with the aim of achieving fault detection. We compare established circuit designs, such as Real Amplitudes and Quantum Convolutional Circuits, as well as hybrid models that combine quantum circuits and neural networks using the library of TensorFlow with Cirq in python. We obtain results on classical computers using analytical calculations that simulate a quantum computer without experiencing the "quantum noise" delivered by the hardware. The most effective way to input data's key features is through a ZFeatureMap circuit, which assigns a qubit to each extracted feature from the data. Quantum convolutional circuits yield better results than other parameterized circuits, while hybrid models provide higher accuracy than their counterparts that do not utilize neural networks. The achieved accuracy rates on the training dataset are around 96%, suggesting that the parameterized quantum circuits used in this master's thesis yield stable results.
Quantum algorithms have become a popular research domain in recent times for discovering quantum-enhanced solutions in machine learning applications. Quantum kernels are one of the directions that establish such quantum-enhanced solutions to some extent. This work presents a detailed analysis of the quantum kernel approach leveraging feature maps and relevant hyperparameters to develop enhanced quantum kernels. The study includes a new high-order feature map and assesses five existing state-of-the-art feature maps for enhanced quantum kernel classifiers. Additionally, the significance of the rotational factor as a hyperparameter is highlighted for improving kernel performance. Also, it is analyzed whether different hyperparameter-tuned feature maps can lead to enhanced decision boundaries, demonstrating kernel expressivity. The analysis is undertaken on classification tasks using four different nonlinear datasets of distinct complexity. Comparative evaluations are also made with traditional machine learning models-Support Vector Machines (Linear and RBF), Naïve Bayes, Linear Discriminant Analysis, Decision Tree, Random Forest, Adaptive Boosting, and MLP. Overall, the study demonstrates that a well-tuned quantum feature map can enhance the generalization ability of quantum kernels, making them more effective for broader quantum-enhanced machine learning applications.
Quantum computing is a new field that has recently attracted researchers from a broad range of fields due to its representation power, flexibility and promising results in both speed and scalability. Since 2020, laboratories around the globe have started to experiment with models that lie in the juxtaposition between machine learning and quantum computing. The availability of quantum processing units (QPUs) to the general scientific community through open API’s (e.g., IBM’s Qiskit) have kindled the interest in developing and testing new approaches to old problems. In this paper, we present a hybrid quantum machine learning framework for health state diagnostics and prognostics. The framework is exemplified using a problem involving ball bearings dataset. To the best of our knowledge, this is the first attempt to harvest and leverage quantum computing to develop and apply a hybrid quantum-classical machine learning approach to a prognostics and health management (PHM) problem. We hope that this paper initiates the exploration and application of quantum machine learning algorithms in areas of risk and reliability.
Quantum kernel methods are a promising branch of quantum machine learning, but their per- formance across data types and encoding choices remains incompletely understood. To address this gap, we developed an algorithmic framework for variational quantum kernels utilizing resource- efficient ansätze for complex classification tasks and introduced a parameter scaling technique to accelerate convergence. We conducted a comprehensive benchmark of this framework on eight chal- lenging, real-world and high-dimensional datasets covering tabular, image, time series, and graph data. Our results show that the proposed quantum kernel demonstrated competitive classification accuracy compared to standard classical kernels in classical simulation, such as the radial basis function (RBF) kernel. This work demonstrates that properly designed quantum kernels can func- tion as versatile, high-performance tools, laying a foundation for quantum-enhanced applications in machine learning. Further research is needed to fully assess the practical performance of quantum methods.