Tool condition monitoring in an end-milling operation based on the vibration signal collected through a microcontroller-based data acquisition system

2007 ◽  
Vol 39 (1-2) ◽  
pp. 118-128 ◽  
Author(s):  
Julie Z. Zhang ◽  
Joseph C. Chen
Keyword(s):  
Data Acquisition ◽  
Condition Monitoring ◽  
End Milling ◽  
Vibration Signal ◽  
Data Acquisition System ◽  
Tool Condition Monitoring ◽  
Acquisition System ◽  
Tool Condition ◽  
Milling Operation

scholarly journals Low-Cost, High-Frequency, Data Acquisition System for Condition Monitoring of Rotating Machinery through Vibration Analysis-Case Study

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3493
Author(s):  
César Ricardo Soto-Ocampo ◽  
José Manuel Mera ◽  
Juan David Cano-Moreno ◽  
José Luis Garcia-Bernardo
Keyword(s):  
Data Acquisition ◽  
Condition Monitoring ◽  
Vibration Analysis ◽  
Low Cost ◽  
Data Acquisition System ◽  
Sampling Frequency ◽  
Rotating Machinery ◽  
Acquisition System ◽  
Raspberry Pi

Data acquisition is a crucial stage in the execution of condition monitoring (CM) of rotating machinery, by means of vibration analysis. However, the major challenge in the execution of this technique lies in the features of the recording equipment (accuracy, resolution, sampling frequency and number of channels) and the cost they represent. The present work proposes a low-cost data acquisition system, based on Raspberry-Pi, with a high sampling frequency capacity in the recording of up to three channels. To demonstrate the effectiveness of the proposed data acquisition system, a case study is presented in which the vibrations registered in a bearing are analyzed for four degrees of failure.

Design of Vibration Signal Data Acquisition System for Rolling Bearing Life Testing Machine Based on Virtual Instrument Technology

2011 ◽  
Vol 328-330 ◽  
pp. 1963-1967
Author(s):  
Guang Li Yu ◽  
Li Hui Dang
Keyword(s):  
Data Acquisition ◽  
Virtual Instrument ◽  
Testing Machine ◽  
Vibration Signal ◽  
Data Acquisition System ◽  
Rolling Bearing ◽  
Acquisition System ◽  
Time Interval ◽  
Life Testing ◽  
Bearing Life

The paper introduces the status of rolling bearing life testing machine in China first. Then, the paper mainly focuses on the design of vibration signal data acquisition system for rolling bearing life testing machine. The system is developed for a rolling bearing manufacturer based on Labview with perfect functions according to the requirement of the customer. The rolling bearing manufacturer can set up different measurement time and time interval for data record to carry out test according to the requirement of its customers. The developed vibration signal acquisition system is not subject to testing machine, which can run only that the sensors are installed on the measurement locations of testing machine. The developed system can be used for different rolling bearing life testing machine, so it has good application perspective in future.

Tool Condition Monitoring Based on Radial Basis Probabilistic Neural Networks and Improved Genetic Algorithm

2010 ◽  
Vol 139-141 ◽  
pp. 2522-2526
Author(s):  
Deng Wan Li ◽  
Hong Li Gao ◽  
Yun Shou ◽  
Peng Du ◽  
Ming Heng Xu
Keyword(s):  
Genetic Algorithm ◽  
Condition Monitoring ◽  
Recognition Rate ◽  
Principal Component ◽  
Vibration Signal ◽  
Tool Condition Monitoring ◽  
Cutting Condition ◽  
Improved Genetic Algorithm ◽  
Tool Condition ◽  
Radial Basis

In order to accurately estimate tool life for milling operation, a novel tool condition monitoring system was proposed to improve classifying precision in different cutting condition. Lots of features were extracted from cutting forces signal, vibration signal and acoustic emission signal by different signal processing method, only a few features selected by principal component analysis (PCA) according to contribution rate, and constructed as input vector. The relation between tool condition and features was built by radial basis probability neural network which control parameter of kernel function and hidden central vector were optimized by improved genetic algorithm. The experimental results show that the method proposed in the paper achieves higher recognition rate, good generalization ability and better available practicality.

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