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Appl. Sci. 2017, 7(4), 346; doi:10.3390/app7040346

Numerical Control Machine Tool Fault Diagnosis Using Hybrid Stationary Subspace Analysis and Least Squares Support Vector Machine with a Single Sensor

College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China
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Author to whom correspondence should be addressed.
Academic Editor: David He
Received: 25 February 2017 / Revised: 23 March 2017 / Accepted: 28 March 2017 / Published: 31 March 2017
(This article belongs to the Special Issue Deep Learning Based Machine Fault Diagnosis and Prognosis)
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Abstract

Tool fault diagnosis in numerical control (NC) machines plays a significant role in ensuring manufacturing quality. However, current methods of tool fault diagnosis lack accuracy. Therefore, in the present paper, a fault diagnosis method was proposed based on stationary subspace analysis (SSA) and least squares support vector machine (LS-SVM) using only a single sensor. First, SSA was used to extract stationary and non-stationary sources from multi-dimensional signals without the need for independency and without prior information of the source signals, after the dimensionality of the vibration signal observed by a single sensor was expanded by phase space reconstruction technique. Subsequently, 10 dimensionless parameters in the time-frequency domain for non-stationary sources were calculated to generate samples to train the LS-SVM. Finally, the measured vibration signals from tools of an unknown state and their non-stationary sources were separated by SSA to serve as test samples for the trained SVM. The experimental validation demonstrated that the proposed method has better diagnosis accuracy than three previous methods based on LS-SVM alone, Principal component analysis and LS-SVM or on SSA and Linear discriminant analysis. View Full-Text
Keywords: stationary subspace analysis; least squares support vector machine; NC machine; tool fault diagnosis stationary subspace analysis; least squares support vector machine; NC machine; tool fault diagnosis
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Gao, C.; Xue, W.; Ren, Y.; Zhou, Y. Numerical Control Machine Tool Fault Diagnosis Using Hybrid Stationary Subspace Analysis and Least Squares Support Vector Machine with a Single Sensor. Appl. Sci. 2017, 7, 346.

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