Prognostic diagnosis of ball screw preload loss for machine tool through the Hilbert-Huang transform and multiscale entropy measure

Yi Cheng Huang, Jun Liang Chang

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Detection of ball screw preload loss for machine tool has great significances in modern manufacturing processes for unexpected failures. This paper proposes a diagnostic method for ball screw preload loss through the Hilbert-Huang transform (HHT) and multiscale entropy(MSE) process. The method is used to diagnose a ball screw preload loss through the motor torque current signals when machine tool is in operation. Maximum dynamic preloads of 2%, 4%, and 6% ball screws were predesigned, manufactured, and conducted experimentally. Signal patterns are discussed and revealed by Empirical Mode Decomposition (EMD) with the Hilbert Spectrum. Different preload features are extracted and discriminated using HHT. The irregularity development of ball screw with preload loss is determined and abstracted via MSE based on complexity perception. The experiment results successfully show that the prognostic status of ball screw preload loss can be envisaged by the proposed methodology. The smart sensing for the health of the ball screw is available based on comparative evaluation of MSE by the signal processing and pattern matching of EMD/HHT. This diagnosis method realizes the purposes of prognostic effectiveness on knowing the preload loss and utilizing convenience.

Original languageEnglish
Pages (from-to)349-358
Number of pages10
JournalJournal of the Chinese Society of Mechanical Engineers, Transactions of the Chinese Institute of Engineers, Series C/Chung-Kuo Chi Hsueh Kung Ch'eng Hsuebo Pao
Volume32
Issue number4
Publication statusPublished - 2011 Aug 1

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Ball screws
Machine tools
Entropy
Decomposition
Torque motors
Pattern matching
Signal processing
Health

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering

Cite this

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title = "Prognostic diagnosis of ball screw preload loss for machine tool through the Hilbert-Huang transform and multiscale entropy measure",
abstract = "Detection of ball screw preload loss for machine tool has great significances in modern manufacturing processes for unexpected failures. This paper proposes a diagnostic method for ball screw preload loss through the Hilbert-Huang transform (HHT) and multiscale entropy(MSE) process. The method is used to diagnose a ball screw preload loss through the motor torque current signals when machine tool is in operation. Maximum dynamic preloads of 2{\%}, 4{\%}, and 6{\%} ball screws were predesigned, manufactured, and conducted experimentally. Signal patterns are discussed and revealed by Empirical Mode Decomposition (EMD) with the Hilbert Spectrum. Different preload features are extracted and discriminated using HHT. The irregularity development of ball screw with preload loss is determined and abstracted via MSE based on complexity perception. The experiment results successfully show that the prognostic status of ball screw preload loss can be envisaged by the proposed methodology. The smart sensing for the health of the ball screw is available based on comparative evaluation of MSE by the signal processing and pattern matching of EMD/HHT. This diagnosis method realizes the purposes of prognostic effectiveness on knowing the preload loss and utilizing convenience.",
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