A Quantum-inspired Evolutionary Algorithm with a competitive variation operator for Multiple-Fault Diagnosis

Arpaïa, Pasquale; Maisto, Domenico; Manna, Carlo

doi:10.1016/j.asoc.2011.07.017

Cited by 26 publications

(9 citation statements)

References 13 publications

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“…which the coefficients α and β indicate the amplitudes of quantum state probability (real number or complex number). jαj 2 and jβj 2 denote the occurrence probability of the quantum ground states j0⟩ and j1⟩ and satisfy the normalization condition, 43 that is,…”

Section: Quantum Theorymentioning

confidence: 99%

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Quantum entropy‐based hierarchical strategy for inter‐shaft bearing fault detection

Jun

Fei

et al. 2021

Struct Control Health Monit

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To effectively conduct the fault diagnosis of inter-shaft bearings in precision and stability, hierarchical quantum entropy (HQE) method is proposed by absorbing quantum theory into hierarchical entropy, to precisely extract the features of fault signals with strong robustness. Firstly, we investigate HQE method and HQE-based fault diagnosis thought based on quantum theory and hierarchical thought. Then the HQE method is validated by numerical simulation in feature extraction precision and stability (robustness) under the influence of key parameters. The HQE method is applied to the fault diagnosis of inter-shaft bearing based on the test data of four faults (i.e., normal state, inner ring fault, outer ring fault, and rolling ball fault) simulated in birotor experimental rig. As revealed in this study, (1) the HQE method can precisely extract and fully reflect the feature information of vibration signals by regarding the information of full-scale components comprising low-and high-frequency components and a series of bit ground states; (2) the HQE of signals is insensitive and stable against the key parameters (i.e., dimension number and data length), due to more scales acquired by quantum entropy and hierarchical decomposition, which is promising to improve the robustness and stability of fault diagnosis; (3) the proposed HQE possesses high diagnostic accuracy, high efficiency, and robustness under few experiment data, which indicates the good diagnostic performance of HQE method in the fault diagnosis of inter-shaft bearing. The efforts of this study provide a useful way to effectively conduct structural health monitoring besides inter-shaft bearing fault diagnosis.

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Section: Quantum Theorymentioning

confidence: 99%

“…Based on the quantum theory, [38][39][40][41][42][43] the state of the vibration signal is analyzed by mapping the vibration signal from time domain space to quantum space. The quantum expression of normalized vibration signal is…”

Section: Quantum Theorymentioning

confidence: 99%

Quantum entropy‐based hierarchical strategy for inter‐shaft bearing fault detection

Jun

Fei

et al. 2021

Struct Control Health Monit

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“…In particular, steady progress in quantum computation technology encourages us to apply and extend the above quantum algorithms to the computationally difficult and important problem of medical diagnostics. Quantum representations can be useful even within a classical computation [ 113 , 114 , 115 , 116 , 117 ]. For instance, an exponentially large number of classical states can be coded in the quantum state or wave function of a linear number of quantum binary systems (qbits).…”

Section: Future Perspectivesmentioning

confidence: 99%

Statistical Physics for Medical Diagnostics: Learning, Inference, and Optimization Algorithms

et al. 2020

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It is widely believed that cooperation between clinicians and machines may address many of the decisional fragilities intrinsic to current medical practice. However, the realization of this potential will require more precise definitions of disease states as well as their dynamics and interactions. A careful probabilistic examination of symptoms and signs, including the molecular profiles of the relevant biochemical networks, will often be required for building an unbiased and efficient diagnostic approach. Analogous problems have been studied for years by physicists extracting macroscopic states of various physical systems by examining microscopic elements and their interactions. These valuable experiences are now being extended to the medical field. From this perspective, we discuss how recent developments in statistical physics, machine learning and inference algorithms are coming together to improve current medical diagnostic approaches.

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“…Various forms of QIEAs have been used to solve a variety of d ifficult problems for examp le [2,3,4,5,6,7,8,9,10,11]. QIEAs have been observed as a powerful tool because of their better representation power [12,13], EDA style of functioning [14,15], flexib ility necessary for the inclusion of features appropriate for a g iven problem towards delivering better search performance [15], inherent quality of starting with exploration and gradually shifting towards the exploitation [13].…”

Section: Introductionmentioning

confidence: 99%

Balanced Quantum-Inspired Evolutionary Algorithm for Multiple Knapsack Problem

Patvardhan¹,

Bansal²,

Srivastav³

2014

IJISA

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0/1 M ultiple Knapsack Problem, a generalization of more popular 0/1 Knapsack Problem, is NP-hard and considered harder than simple Knapsack Problem. 0/1 M ultiple Knapsack Problem has many applications in disciplines related to computer science and operations research. Quantum Inspired Evolutionary Algorithms (QIEAs), a subclass of Evolutionary algorithms, are considered effective to solve difficult problems particularly NP-hard combinatorial optimization problems. A hybrid QIEA is presented for multiple knapsack problem which incorporates several features for better balance between exploration and exploitation. The proposed QIEA, dubbed QIEA-M KP, provides significantly improved performance over simple QIEA from both the perspectives viz., the quality of solutions and computational effort required to reach the best solution. QIEA-M KP is also able to provide the solutions that are better than those obtained using a well known heuristic alone.

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A Quantum-inspired Evolutionary Algorithm with a competitive variation operator for Multiple-Fault Diagnosis

Cited by 26 publications

References 13 publications

Quantum entropy‐based hierarchical strategy for inter‐shaft bearing fault detection

Quantum entropy‐based hierarchical strategy for inter‐shaft bearing fault detection

Statistical Physics for Medical Diagnostics: Learning, Inference, and Optimization Algorithms

Balanced Quantum-Inspired Evolutionary Algorithm for Multiple Knapsack Problem

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