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Vol. 156, Issue 9, September 2013, pp. 195-202

 

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The Diagnosis of Tool Wear Based on EMD and GA-B-Spline Network
 
* Weiqing Cao, Pan Fu, Xiaohui Li

School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, China

* Tel.: 15882258040

* E-mail: caoweiqing@swjtu.cn

 

Received: 5 June 2013   /Accepted: 25 August 2013   /Published: 30 September 2013

Digital Sensors and Sensor Sysstems

 

Abstract: In view of the strong background noise involved in vibration signal of tool wear and the difficulty to obtain fault frequencies, this paper proposed a tool wear fault feature extraction method based on morphological filters-singularity value decomposition (SVD) with empirical mode decomposition (EMD). Firstly, an experiment system of the cutting tool wear monitoring was set up and a variety of data coming from vibratory sensor were collected, then, the pulse components from the original signal were inhibited by morphological filters and the signal sequences removed outlier were reconstructed, the attractor track matrix was decomposed using SVD for further noise reduction, and then we got weak signal failure frequency after the de-noise signals were decomposed with EMD. Finally, tool wear was identified by GA-B-spline neural network. B-spline networks were trained using genetic algorithms to search for global optimization. The experimental results shown that the diagnosis approach put forward in this paper could identify tool wear fault patterns effectively in noise background.

 

Keywords: Empirical mode decomposition; Morphological filtering; Singularity value decomposition; Genetic algorithm; B-spline Neural Networks.

 

 

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