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.
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.
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.
The field of Prognostics and Health Management (PHM) aims to predict the behavior of machines to make informed maintenance decisions. In the Oil and Gas industry, fault mode diagnosis, as a PHM activity, has been applied to rotating machinery such as compressors, centrifugal pumps, and submersible motors using traditional Machine Learning (ML) and Deep Learning techniques. With the emergence of a new and rapidly growing research field called Quantum Computing (QC), there is now potential for even more efficient and accurate predictions. The QC has contributed to different purposes and contexts, such as optimization, artificial intelligence, simulation, cybersecurity, pharmaceutics, and the energy sector. Despite the current limitations of hardware, QC has been explored to improve the speed and efficiency of ML models. This master thesis focuses on the application of Quantum Machine Learning (QML) 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 connected to a classical neural network, the Multi-Layer Perceptron (MLP). The study uses the Variational Quantum Eigensolver framework along with rotation gates and different entanglement (two-qubits) gates (CNOT, CZ and iSWAP), and explores the impact of varying the number of layers (1, 5 and 10) in the quantum circuit. We use two databases of different complexity levels not previously explored with QML, namely Case Western Reserve University (CWRU) and Jiangnan University (JNU), with 10 and 12 failure modes, respectively. For CWRU and JNU, all QML models presented higher accuracy than the classical MLP. These results suggest that, despite the current limitations of quantum environments, QML models are promising tools to be further investigated in PHM activities in the Oil and Gas industry.
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.
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 APIs (e.g., Qiskit from IBM) 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 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.
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.