Monitoring Rotating Tool Wear Using Higher-Order Spectral Features

1993 ◽  
Vol 115 (1) ◽  
pp. 23-29 ◽  
Author(s):  
R. W. Barker ◽  
G. Klutke ◽  
M. J. Hinich
Keyword(s):  
Power Spectrum ◽  
Tool Wear ◽  
Higher Order ◽  
Order Spectrum ◽  
Accelerometer Signal ◽  
Signal Features ◽  
Drill Bits ◽  
Study Results ◽  
Wear Monitoring ◽  
Combine Power

A framework for detecting incipient wear in rotating machinery is proposed. In this paper, statistical techniques that combine power spectrum estimates with higher-order spectrum (HOS) estimates for feature development are applied to discriminate and classify vibration signals from new and slightly used drill bits in a drill wear study. Results from experimental data obtained when drilling composite circuit cards reveal that the performance of a power spectrum-based tool wear monitoring system can be enhanced by complementing the power spectrum information with HOS information on the accelerometer signal. Evidence presented supports the proposition that a HOS approach provides better signal features to a pattern classifier which allows better decisions on the state of rotating tool wear.

Fault diagnosis of rolling bearing based on empirical mode decomposition and higher order statistics

2014 ◽  
Vol 229 (9) ◽  
pp. 1630-1638 ◽  
Author(s):  
Jian-hua Cai
Keyword(s):  
Power Spectrum ◽  
Order Statistics ◽  
Empirical Mode Decomposition ◽  
Gaussian Noise ◽  
Rolling Bearing ◽  
Higher Order ◽  
Intrinsic Mode Function ◽  
Order Spectrum ◽  
Mode Function ◽  
Mode Decomposition

In order to solve the problem of the faulted rolling bearing signal getting easily affected by Gaussian noise, a new fault diagnosis method was proposed based on empirical mode decomposition and high-order statistics. Firstly, the vibration signal was decomposed by empirical mode decomposition and the correlation coefficient of each intrinsic mode function was calculated. These intrinsic mode function components, which have a big correlation coefficient, were selected to estimate its higher order spectrum. Then based on the higher order statistics theory, this method uses higher order spectrum of each intrinsic mode function to reconstruct its power spectrum. And these power spectrums were summed to obtain the primary power spectrum of bearing signal. Finally, fault feature information was extracted from the reconstructed power spectrum. A model, using higher order spectrum to reconstruct power spectrum, was established. Meanwhile, analysis was conducted by using the simulated data and the recorded vibration signals which include inner race, out race, and bearing ball fault signal. Results show that the presented method is superior to traditional power spectrum method in suppressing Gaussian noise and its resolution is higher. New method can extract more useful information compared to the traditional method.

Hierarchical Strategies in Tool Wear Monitoring

2006 ◽  
Vol 220 (3) ◽  
pp. 375-381 ◽  
Author(s):  
K Jemielniak ◽  
S Bombiński
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Tool Wear Monitoring ◽  
Second Stage ◽  
Monitoring Strategies ◽  
Signal Features ◽  
Input Signals ◽  
Wear Monitoring ◽  
Wear Evaluation ◽  
Industrial Solutions

The paper presents a comparison of efficiency of tool wear monitoring strategies based on one signal feature, on a single neural network with several input signals, and on a hierarchical algorithm and a large number of signal features. In the first stage of the hierarchical algorithms, the tool wear was estimated separately for each signal feature. This stage was carried out using either simple neural networks or polynomial approximation. In the second stage, the results obtained in the first one, were integrated into the final tool wear evaluation. The integration was carried out by the use of either a neural network or averaging. The paper shows a considerable advantage of the hierarchical models over conventional industrial solutions (single signal feature) and typical laboratory solutions (single, large neural network).

scholarly journals Application of Bispectrum Diagonal Slice Feature Analysis in Tool Wear States Monitoring

2021 ◽  
Author(s):  
Bin Yang ◽  
Min Wang ◽  
Tao Zan ◽  
Xiangsheng Gao ◽  
Peng Gao
Keyword(s):  
Tool Wear ◽  
Failure Modes ◽  
Vibration Signal ◽  
Higher Order ◽  
Cnc Machining ◽  
Monitoring Method ◽  
Order Spectrum ◽  
Milling Tool ◽  
Cutting Vibration ◽  
Non Gaussian

Abstract Tool wear is unavoidable during machining, which is one of the most common tool failure modes. It is significant to evaluate the tool state quickly and effectively for timely tool change strategy. The cutting vibration signals after tool wear show strong non-Gaussian characteristics. Higher order spectrum is a powerful tool for analyzing the non-Gaussian characteristics of signals, and can restrain noise and provide more information than classical power spectrum analysis. This paper presents a milling tool wear state monitoring method based on higher order spectrum entropy. Due to the large amount of calculation of bispectrum, bispectrum diagonal slice is investigated. And the diagonal slice spectral entropy is proposed as tool wear indicator to monitor tool state. To verify the proposed method, cutting vibration signal of CNC machining center were collected and analyzed. The experimental results showed that the proposed approach can effectively monitor and diagnose the tool state, and has good robustness. It is feasible and effective for on-line monitoring milling tool wear.

Tool wear monitoring using acoustic emission in the existence of chatter

1990 ◽  
Vol 28 (10) ◽  
pp. 1861-1869 ◽  
Author(s):  
YOICHI MATSUMOTO ◽  
NGUN TJIANG ◽  
BOBBIE FOOTE ◽  
YNGVE NAERHEIMH
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Tool Wear Monitoring ◽  
Wear Monitoring
Sign in / Sign up

Export Citation Format

Share Document