Multi-scale Permutation Entropy Based On-line Milling Chatter Detection Method

2015 ◽  
Vol 51 (9) ◽  
pp. 206 ◽  
Author(s):  
Jingbo REN
Keyword(s):  
Detection Method ◽  
Permutation Entropy ◽  
Chatter Detection ◽  
Multi Scale ◽  
On Line ◽  
Milling Chatter

On-Line Energy-Based Milling Chatter Detection

2018 ◽  
Vol 140 (11) ◽  
Author(s):  
Hakan Caliskan ◽  
Zekai Murat Kilic ◽  
Yusuf Altintas
Keyword(s):  
Fourier Transforms ◽  
Energy Operator ◽  
Forced Vibrations ◽  
Chatter Detection ◽  
Line Energy ◽  
Natural Modes ◽  
Chatter Vibrations ◽  
On Line ◽  
Milling Chatter ◽  
Periodic Components

Milling exhibits forced vibrations at tooth passing frequency and its harmonics, as well as chatter vibrations close to one of the natural modes. In addition, there are sidebands, which are spread at the multiples of tooth passing frequency above and below the chatter frequency, and make the robust chatter detection difficult. This paper presents a novel on-line chatter detection method by monitoring the vibration energy. Forced vibrations are removed from the measurements in discrete time domain using a Kalman filter. After removing all periodic components, the amplitude and frequency of chatter are searched in between the two consecutive tooth passing frequency harmonics using a nonlinear energy operator (NEO). When the energy of any chatter component grows relative to the energy of forced vibrations, the presence of chatter is detected. The proposed method works in discrete real time intervals, and can detect the chatter earlier than frequency domain-based methods, which rely on fast Fourier Transforms. The method has been experimentally validated in several milling tests using both microphone and accelerometer measurements, as well as using spindle speed and current signals.

A Model Based Computationally Efficient Method for On-Line Detection of Chatter in Milling

2013 ◽  
Author(s):  
Lei Ma ◽  
Shreyes Melkote ◽  
James Castle
Keyword(s):  
Efficient Method ◽  
Chip Thickness ◽  
Line Detection ◽  
Chatter Detection ◽  
Computationally Efficient ◽  
Model Based ◽  
Force Signal ◽  
On Line ◽  
Regeneration Effect ◽  
Milling Chatter

This paper presents a model-based computationally efficient method for detecting milling chatter in its incipient stages. Based on a complex exponentials model for the dynamic chip thickness, the chip regeneration effect is amplified and isolated from the cutting force signal for early chatter detection. The proposed method is independent of the cutting conditions. With the aid of a one tap adaptive filter, the proposed method is also found to be able to distinguish between chatter and the dynamic transients in the cutting forces due to sudden changes in workpiece geometry and tool entry/exit. The proposed method is experimentally validated.

scholarly journals A Novel Arc Fault Detection Method Integrated Random Forest, Improved Multi-scale Permutation Entropy and Wavelet Packet Transform

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 396 ◽  
Author(s):  
Zhendong Yin ◽  
Li Wang ◽  
Yaojia Zhang ◽  
Yang Gao
Keyword(s):  
Random Forest ◽  
Fault Detection ◽  
Detection Method ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Permutation Entropy ◽  
High Dimensional ◽  
Multi Scale ◽  
Fault Features ◽  
Load Types

Arc faults are one of the important causes of electric fires. In order to solve the problem of randomness, diversity, the concealment of series arc faults and to improve the detection accuracy, a novel arc fault detection method integrated random forest (RF), improved multi-scale permutation entropy (IMPE) and wavelet packet transform (WPT) are designed. Firstly, singular value decomposition (SVD) was applied to filter the current signal and then the high-dimensional fault features were constructed by extracting IMPE, the wavelet packet energy and the wavelet packet energy-entropy. Afterward, the high-dimensional fault features were employed to train the RF to realize the arc fault detection of different load types and the experimental results verify the effectiveness of the arc fault detection method designed in this paper. Finally, the comparative experiments demonstrates that the RF shows better performance in arc fault detection compared to the back-propagation neural network (BPNN) and least squares support vector machines (LSSVM), and that the experiments of transient events indicate that RF is able to effectively avoid incorrectly detecting different load types during the start operations and stop operations.

Research Situation Analysis of On-line Detection Technology for Power Transformer Winding Deformation

2020 ◽  
Vol 13 (6) ◽  
pp. 823-832
Author(s):  
Zhenhua Li ◽  
Weihui Jiang ◽  
Li Qiu ◽  
Zhenxing Li ◽  
Yanchun Xu
Keyword(s):  
Detection Method ◽  
Theoretical Research ◽  
Line Detection ◽  
Response Analysis ◽  
Advantages And Disadvantages ◽  
Detection Technology ◽  
The Future ◽  
Transformer Winding ◽  
On Line ◽  
Winding Deformation

Background: Winding deformation is one of the most common faults in power transformers, which seriously threatens the safe operation of transformers. In order to discover the hidden trouble of transformer in time, it is of great significance to actively carry out the research of transformer winding deformation detection technology. Methods: In this paper, several methods of winding deformation detection with on-line detection prospects are summarized. The principles and characteristics of each method are analyzed, and the advantages and disadvantages of each method as well as the future research directions are expounded. Finally, aiming at the existing problems, the development direction of detection method for winding deformation in the future is prospected. Results: The on-line frequency response analysis method is still immature, and the vibration detection method is still in the theoretical research stage. Conclusion: The ΔV − I1 locus method provides a new direction for on-line detection of transformer winding deformation faults, which has certain application prospects and practical engineering value.

Defect detection method for rail surface based on line-structured light

Measurement ◽  
2020 ◽  
Vol 159 ◽  
pp. 107771 ◽  
Author(s):  
Xiaohui Cao ◽  
Wen Xie ◽  
Siddiqui Muneeb Ahmed ◽  
Cun Rong Li
Keyword(s):  
Defect Detection ◽  
Detection Method ◽  
Structured Light ◽  
On Line

Study on Damage Detection of Bridge Based on Wavelet Multi-Scale Analysis

2013 ◽  
Vol 639-640 ◽  
pp. 1010-1014 ◽  
Author(s):  
Ke Ding ◽  
Ting Peng Chen
Keyword(s):  
Wavelet Transform ◽  
Damage Detection ◽  
Detection Method ◽  
Mode Shapes ◽  
Scale Analysis ◽  
Multi Scale ◽  
Damage Location ◽  
Transform Coefficients ◽  
Beam Bridge ◽  
Multi Scale Analysis

The damage detection method based on wavelet multi-scale analysis is presented in the paper. The damage location can be identified by analyzing the multi-scale wavelet transform coefficients of curvatures of mode shapes. The extreme value of wavelet transform coefficients indicates the damage location. But it is difficult to detect the location of defect if the defect is near to the equilibrium position of vibration. In order to solve this problem, we put forward a method which is to add the wavelet transform coefficients of multi modals together. The method can effectively overcome the above problem. Three damage situations of simply supported beam bridge are discussed in the paper. The results show that the peaks of wavelet transform coefficients indicate the damage location of structural. It is possible to pinpoint the damage location based on wavelet multi-scale analysis on curvatures of mode shapes.

Sign in / Sign up

Export Citation Format

Share Document