Sensor Data Modeling and Model Frequency Analysis for Detecting Cutting Tool Anomalies in Machining-Reference-Cited by-同舟云学术

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Sensor Data Modeling and Model Frequency Analysis for Detecting Cutting Tool Anomalies in Machining

Published:2023-05 Issue:5 Volume:53 Page:2641-2653
ISSN:2168-2216
Container-title:IEEE Transactions on Systems, Man, and Cybernetics: Systems
language:
Short-container-title:IEEE Trans. Syst. Man Cybern, Syst.

Author:

Liu Zepeng¹^ORCID,Lang Zi-Qiang¹^ORCID,Zhu Yun-Peng²^ORCID,Gui Yufei¹,Laalej Hatim³,Stammers Jon³

Affiliation:

1. Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, U.K

2. School of Engineering and Materials Science, Queen Mary University of London, London, U.K

3. Advanced Manufacturing Research Centre, The University of Sheffield, Sheffield, U.K

Funder

Engineering and Physical Sciences Research Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Control and Systems Engineering,Software

Link

http://xplorestaging.ieee.org/ielx7/6221021/10102639/09945641.pdf?arnumber=9945641

Reference28 articles.

1. Crack detection using nonlinear output frequency response functions

2. A weighted hidden Markov model approach for continuous-state tool wear monitoring and tool life prediction

3. A novel method of combining nonlinear frequency spectrum and deep learning for complex system fault diagnosis

4. Nonlinear System Identification

5. Online Tool Wear Monitoring Via Hidden Semi-Markov Model With Dependent Durations

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2. Vibration Signal-Based Tool Condition Monitoring Using Regularized Sensor Data Modeling and Model Frequency Analysis;IEEE Transactions on Instrumentation and Measurement;2024

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4. Fault Features Uncertainty Quantification With Parameters Uncertainties of Data-Driven Models and Its Application in Rotor Systems Condition Assessment;IEEE Transactions on Instrumentation and Measurement;2024

5. A Nonlinear AutoRegressive-Based Noise Cancellation Method for Real-Time Fault Diagnosis of Rolling Bearings;IEEE Transactions on Instrumentation and Measurement;2024

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