Condition Feature Extraction of Machine Tools Based on Wavelet Packet Energy Spectrum Analysis and Bispectrum Analysis of Current Signal

2011 ◽  
Vol 101-102 ◽  
pp. 847-850 ◽  
Author(s):  
Teng Fei Fang ◽  
Guo Fu Li
Keyword(s):  
Feature Extraction ◽  
Energy Spectrum ◽  
Real Time ◽  
Spectrum Analysis ◽  
Machine Tools ◽  
Signal Analysis ◽  
Wavelet Packet ◽  
Current Signal ◽  
Load Current ◽  
Working Status

Based on the study of the characteristics of load current signal, this article develops a method to extract features that can be use to distinguish the different working status of machine tools in real-time manner. The features are extracted from wavelet packet energy spectrum and bispectrum of the load current signal, and thus can take advantages of both wavelet packet transforms and bispectrum in signal analysis. Experimental results show that, compared with the features extracted from wavelet packet energy spectrum or bispectrum alone, the features extracted by applying the proposed method can provide better performance in term of identifying the machine working status.

Research on the Imbalance-Crack Coupling Fault Diagnosis Based on Wavelet Packet and Energy Spectrum Analysis

2011 ◽  
Vol 143-144 ◽  
pp. 675-679 ◽  
Author(s):  
Fu Ze Xu ◽  
Xue Jun Li ◽  
Guang Bin Wang ◽  
Da Lian Yang
Keyword(s):  
Mechanical Properties ◽  
Fault Diagnosis ◽  
Energy Spectrum ◽  
Spectrum Analysis ◽  
Wavelet Packet ◽  
Rotating Machinery ◽  
Vibration Acceleration ◽  
Acceleration Signal ◽  
Coupling Fault ◽  
Fault Information

It is common for the imbalance-crack coupling fault in rotating machinery, while the crack information is often overshadowed by unbalanced fault information, which is difficult to extract the crack signal. In order to extract the crack signal of the imbalance-crack coupling fault, and realize the fault diagnosis, the paper mainly analyzes its mechanical properties, and then use wavelet packet to de-nosing, decomposing and reconstructing the acquisition of vibration acceleration signal, and then analyzing the characteristics of frequency domain of the fault signal by using the energy spectrum. So the experiment proved that analyze and dispose the acquisition of the fault signal by using the method of the energy spectrum and the wavelet packet, which can effectively distinguish between the crack signal and unbalanced signals in imbalance-crack coupling faults .It also can provide some reference for the diagnosis and prevention for such fault.

The Monitoring of Machine Tool Working State Based on Load Current Signal

2010 ◽  
Vol 37-38 ◽  
pp. 1512-1515 ◽  
Author(s):  
Guang Lin Yu ◽  
Guo Fu Li
Keyword(s):  
Working Conditions ◽  
Machine Tools ◽  
Working Condition ◽  
Wavelet Packet ◽  
Analytical Signal ◽  
Wavelet Packet Decomposition ◽  
Machine Condition ◽  
Current Signal ◽  
Motor Current ◽  
Machine Condition Monitoring

According to the characteristic of machine tools such as complex driving chain and enclosed housing, this paper selects current signal which is easy to sample as the analytical signal. As the machines tools use different driving chain in different work state, this will affect motor current of machine tools; that is, the characteristics under different working conditions will be included in the current signal. This paper chose wavelet packet decomposition to analyze the current signal, then extracted wavelet packet coefficients of different frequency bands, by the change of wavelet packet coefficient to determine the machine's working condition. From the analysis of lathe current signal sampled in the experiment, it indicates the validity of wavelet packet coefficients as a feature quantity of the machine condition monitoring.

State Monitoring of Lathes Based on Load Current Signal

2012 ◽  
Vol 201-202 ◽  
pp. 758-762
Author(s):  
Yue Ping Yu ◽  
Guang Lin Yu ◽  
Hong Bin Li ◽  
Guo Fu Li
Keyword(s):  
Wavelet Packet ◽  
Wavelet Denoising ◽  
Wavelet Packet Transform ◽  
Current Signal ◽  
State Monitoring ◽  
Denoising Method ◽  
Load Current ◽  
Time Frequency ◽  
Characteristic Quantity ◽  
Motor Load

According to the characteristics of machine tools such as complex driving chain ,weak signal and enclosed housing,this paper takes horizontal lathes as study objects and selects current signal which is easy to sample as the analytical signal.We collect motor load current signals of idling, cylindrical cutting and end cutting processing state in the experiment to process the condition monitoring based on wavelet denoising and wavelet packet transform. We take advantage of the threshold denoising method to reduce noise of load current signal.Then we use time-frequency analysis methods of wavelet packet transform to extract state characteristic quantity and outstand useful information.So in this paper we monitor the working state of lathes based on the unique advantages of wavelet denoising and wavelet packet transform, and this method can be widely used in various fields of state monitoring.

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