Tool Condition Monitoring based on sound and vibration analysis and wavelet packet decomposition

2012 ◽  
Author(s):  
Hamed Rafezi ◽  
Javad Akbari ◽  
Mehdi Behzad
Keyword(s):  
Condition Monitoring ◽  
Vibration Analysis ◽  
Wavelet Packet ◽  
Tool Condition Monitoring ◽  
Wavelet Packet Decomposition ◽  
Tool Condition

A New Method for Real-Time Tool Condition Monitoring in Transfer Machining Stations1

2000 ◽  
Vol 123 (2) ◽  
pp. 339-347 ◽  
Author(s):  
Ya Wu ◽  
Philippe Escande ◽  
R. Du
Keyword(s):  
Decision Making ◽  
Condition Monitoring ◽  
Wavelet Packet ◽  
Signal Reconstruction ◽  
Spindle Motor ◽  
Tool Condition Monitoring ◽  
New Method ◽  
Wavelet Packets ◽  
Tool Condition ◽  
Threshold Crossing

This paper introduces a new method for tool condition monitoring in transfer machining stations. The new method is developed based on a combination of wavelet transform, signal reconstruction, and the probability of threshold crossing. It consists of two parts: training and decision making. Training is aimed at determining the alarm threshold and it is done in six steps: (1) Calculate the wavelet packet transform of the sensor signals (spindle motor current) obtained from normal tool conditions. (2) Select feature wavelet packets that represent the principal components of the signals. (3) Reconstruct the signals from the feature wavelet packets (this removes the unwanted noises). (4) Calculate the statistics of the reconstructed signals. (5) Calculate the alarm thresholds based on the statistics of the reconstructed signals, and (6) Calculate the probability of the threshold crossing (the number of threshold crossing conforms a Poisson distribution). The decision making is done in two steps: (1) Check the threshold crossing, and (2) Calculate the number of threshold crossing to determine whether an alarm shall be given. As demonstrated using a practical example from a drilling transfer station, the new method is effective with a success rate over 90 percent. Also, it is fast (the monitoring decision can be done in milliseconds) and cost-effective (the implementation cost shall be less than $500).

scholarly journals Artificial Intelligence-Based Hole Quality Prediction in Micro-Drilling Using Multiple Sensors

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 885 ◽  
Author(s):  
Jitesh Ranjan ◽  
Karali Patra ◽  
Tibor Szalay ◽  
Mozammel Mia ◽  
Munish Kumar Gupta ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Fuzzy Inference ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Tool Condition Monitoring ◽  
Hole Quality ◽  
Inference System ◽  
Tool Condition ◽  
Micro Drilling ◽  
Sensor Signals

The prevalence of micro-holes is widespread in mechanical, electronic, optical, ornaments, micro-fluidic devices, etc. However, monitoring and detection tool wear and tool breakage are imperative to achieve improved hole quality and high productivity in micro-drilling. The various multi-sensor signals are used to monitor the condition of the tool. In this work, the vibration signals and cutting force signals have been applied individually as well as in combination to determine their effectiveness for tool-condition monitoring applications. Moreover, they have been used to determine the best strategies for tool-condition monitoring by prediction of hole quality during micro-drilling operations with 0.4 mm micro-drills. Furthermore, this work also developed an adaptive neuro fuzzy inference system (ANFIS) model using different time domains and wavelet packet features of these sensor signals for the prediction of the hole quality. The best prediction of hole quality was obtained by a combination of different sensor features in wavelet domain of vibration signal. The model’s predicted results were found to exert a good agreement with the experimental results.

scholarly journals Carbide Coated Insert Health Monitoring Using Machine Learning Approach through Vibration Analysis

2020 ◽  
Vol 8 (2) ◽  
Author(s):  
Navneet Bohara ◽  
Jegadeeshwaran. R ◽  
Sakthivel G
Keyword(s):  
Machine Learning ◽  
Condition Monitoring ◽  
Vibration Analysis ◽  
Manufacturing Sector ◽  
Health Condition ◽  
Tool Condition Monitoring ◽  
Learning Approach ◽  
Tool Condition ◽  
Machine Learning Approach ◽  
Carbide Inserts

Growth in the manufacturing sector demands extensive production with precision, accuracy, tolerance, and quality. These essential factors need to be ensured for any kind of job. The listed factors stated above depend upon the condition of the tool used for manufacturing. A lot of methods have been proposed for the tool condition monitoring, based on the data acquired through acquisition techniques. Despite the continuous intensive scientific research for more than a decade, the development of tool condition monitoring is an on-going attempt. The proposed method deals with monitoring the health condition of the carbide inserts using vibration analysis. The statistical information extracted from the vibration signals was analyzed using machine learning approach in order to predict the tool condition.

On-line manipulator tool condition monitoring based on vibration analysis

2017 ◽  
Vol 89 ◽  
pp. 14-26 ◽  
Author(s):  
Piotr Gierlak ◽  
Andrzej Burghardt ◽  
Dariusz Szybicki ◽  
Marcin Szuster ◽  
Magdalena Muszyńska
Keyword(s):  
Condition Monitoring ◽  
Vibration Analysis ◽  
Tool Condition Monitoring ◽  
Tool Condition ◽  
On Line

scholarly journals Remote acoustic analysis for tool condition monitoring

2019 ◽  
Vol 38 ◽  
pp. 840-847
Author(s):  
James Coady ◽  
Daniel Toal ◽  
Thomas Newe ◽  
Gerard Dooly
Keyword(s):  
Condition Monitoring ◽  
Acoustic Analysis ◽  
Tool Condition Monitoring ◽  
Tool Condition
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