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

Summary 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 i...

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Veröffentlicht in:Structural control and health monitoring 2021-12, Vol.28 (12), p.n/a
Hauptverfasser: Tian, Jing, Yi, Guo‐Wei, Fei, Cheng‐Wei, Zhou, Jie, Ai, Yan‐Ting, Zhang, Feng‐Ling
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container_issue 12
container_start_page
container_title Structural control and health monitoring
container_volume 28
creator Tian, Jing
Yi, Guo‐Wei
Fei, Cheng‐Wei
Zhou, Jie
Ai, Yan‐Ting
Zhang, Feng‐Ling
description Summary 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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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. 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subjects Diagnostic systems
Entropy
Fault detection
Fault diagnosis
Feature extraction
hierarchical quantum entropy
inter‐shaft bearing
Mathematical models
Parameters
Quantum theory
Robustness (mathematics)
Stability
Structural health monitoring
title Quantum entropy‐based hierarchical strategy for inter‐shaft bearing fault detection
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