A module-based software system for spindle condition monitoring

Ruqiang Yan; Robert X. Gao; Li Zhang; Kang B. Lee

doi:10.1504/ijmms.2009.028079

A module-based software system for spindle condition monitoring

Ruqiang Yan
, Robert X. Gao
, Li Zhang
, Kang B. Lee

University of Connecticut
General Electric
Global Research Centre of the General Electric Corporation
National Institute of Standards and Technology

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

Accurate identification of spindle working conditions is one of the key features of the next generation smart machining systems with built-in, self-diagnosis capability. This paper presents a module-based software system for online spindle defect identification and localisation through an analytic wavelet envelope spectrum algorithm. The software is designed in accordance with the architectural structure of OSA-CBM, and implemented using the graphical programming language LabVIEW. Spindle condition is displayed online in both a basic window for machine operators and a diagnosis window for advanced analysis. The software provides a user-friendly human-machine interface and contributes to realising a smart machine tool.

Original language	English
Pages (from-to)	532-551
Number of pages	20
Journal	International Journal of Mechatronics and Manufacturing Systems
Volume	2
Issue number	5-6
DOIs	https://doi.org/10.1504/ijmms.2009.028079
State	Published - Sep 2009
Externally published	Yes

Keywords

Analytic wavelet; ssi
Modular software design
Osa/cbm architecture
Smart machining system
Spindle condition monitoring
Stochastic subspace identification

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10.1504/ijmms.2009.028079

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title = "A module-based software system for spindle condition monitoring",

abstract = "Accurate identification of spindle working conditions is one of the key features of the next generation smart machining systems with built-in, self-diagnosis capability. This paper presents a module-based software system for online spindle defect identification and localisation through an analytic wavelet envelope spectrum algorithm. The software is designed in accordance with the architectural structure of OSA-CBM, and implemented using the graphical programming language LabVIEW. Spindle condition is displayed online in both a basic window for machine operators and a diagnosis window for advanced analysis. The software provides a user-friendly human-machine interface and contributes to realising a smart machine tool.",

keywords = "Analytic wavelet; ssi, Modular software design, Osa/cbm architecture, Smart machining system, Spindle condition monitoring, Stochastic subspace identification",

author = "Ruqiang Yan and Gao, \{Robert X.\} and Li Zhang and Lee, \{Kang B.\}",

year = "2009",

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T1 - A module-based software system for spindle condition monitoring

AU - Yan, Ruqiang

AU - Gao, Robert X.

AU - Zhang, Li

AU - Lee, Kang B.

PY - 2009/9

Y1 - 2009/9

N2 - Accurate identification of spindle working conditions is one of the key features of the next generation smart machining systems with built-in, self-diagnosis capability. This paper presents a module-based software system for online spindle defect identification and localisation through an analytic wavelet envelope spectrum algorithm. The software is designed in accordance with the architectural structure of OSA-CBM, and implemented using the graphical programming language LabVIEW. Spindle condition is displayed online in both a basic window for machine operators and a diagnosis window for advanced analysis. The software provides a user-friendly human-machine interface and contributes to realising a smart machine tool.

AB - Accurate identification of spindle working conditions is one of the key features of the next generation smart machining systems with built-in, self-diagnosis capability. This paper presents a module-based software system for online spindle defect identification and localisation through an analytic wavelet envelope spectrum algorithm. The software is designed in accordance with the architectural structure of OSA-CBM, and implemented using the graphical programming language LabVIEW. Spindle condition is displayed online in both a basic window for machine operators and a diagnosis window for advanced analysis. The software provides a user-friendly human-machine interface and contributes to realising a smart machine tool.

KW - Analytic wavelet; ssi

KW - Modular software design

KW - Osa/cbm architecture

KW - Smart machining system

KW - Spindle condition monitoring

KW - Stochastic subspace identification

UR - https://www.scopus.com/pages/publications/70349201129

U2 - 10.1504/ijmms.2009.028079

DO - 10.1504/ijmms.2009.028079

M3 - 文章

AN - SCOPUS:70349201129

SN - 1753-1039

VL - 2

SP - 532

EP - 551

JO - International Journal of Mechatronics and Manufacturing Systems

JF - International Journal of Mechatronics and Manufacturing Systems

IS - 5-6

ER -

A module-based software system for spindle condition monitoring

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Keywords

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