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.

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.

scholarly journals Research on a Real-Time Monitoring Method for the Wear State of a Tool Based on a Convolutional Bidirectional LSTM Model

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1233 ◽  
Author(s):  
Chen ◽  
Xie ◽  
Yuan ◽  
Huang ◽  
Li
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Tool Wear ◽  
Real Time ◽  
Original Data ◽  
Attention Mechanism ◽  
Cnc Machining ◽  
Numerical Control ◽  
Real Time Monitoring ◽  
Monitoring Method

To monitor the tool wear state of computerized numerical control (CNC) machining equipment in real time in a manufacturing workshop, this paper proposes a real-time monitoring method based on a fusion of a convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) network with an attention mechanism (CABLSTM). In this method, the CNN is used to extract deep features from the time-series signal as an input, and then the BiLSTM network with a symmetric structure is constructed to learn the time-series information between the feature vectors. The attention mechanism is introduced to self-adaptively perceive the network weights associated with the classification results of the wear state and distribute the weights reasonably. Finally, the signal features of different weights are sent to a Softmax classifier to classify the tool wear state. In addition, a data acquisition experiment platform is developed with a high-precision CNC milling machine and an acceleration sensor to collect the vibration signals generated during tool processing in real time. The original data are directly fed into the depth neural network of the model for analysis, which avoids the complexity and limitations caused by a manual feature extraction. The experimental results show that, compared with other deep learning neural networks and traditional machine learning network models, the model can predict the tool wear state accurately in real time from original data collected by sensors, and the recognition accuracy and generalization have been improved to a certain extent.

scholarly journals Higher-Order Spectrum in Understanding Nonlinearity in EEG Rhythms

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Cauchy Pradhan ◽  
Susant K. Jena ◽  
Sreenivasan R. Nadar ◽  
N. Pradhan
Keyword(s):  
Time Series ◽  
Higher Order ◽  
Nonlinear Coupling ◽  
Spectral Estimates ◽  
Order Spectrum ◽  
Non Gaussian ◽  
Electroencephalogram Eeg ◽  
Electrical Activities ◽  
Background Eeg ◽  
Normal Background

The fundamental nature of the brain's electrical activities recorded as electroencephalogram (EEG) remains unknown. Linear stochastic models and spectral estimates are the most common methods for the analysis of EEG because of their robustness, simplicity of interpretation, and apparent association with rhythmic behavioral patterns in nature. In this paper, we extend the use of higher-order spectrum in order to indicate the hidden characteristics of EEG signals that simply do not arise from random processes. The higher-order spectrum is an extension Fourier spectrum that uses higher moments for spectral estimates. This essentially nullifies all Gaussian random effects, therefore, can reveal non-Gaussian and nonlinear characteristics in the complex patterns of EEG time series. The paper demonstrates the distinguishing features of bispectral analysis for chaotic systems, filtered noises, and normal background EEG activity. The bispectrum analysis detects nonlinear interactions; however, it does not quantify the coupling strength. The squared bicoherence in the nonredundant region has been estimated to demonstrate nonlinear coupling. The bicoherence values are minimal for white Gaussian noises (WGNs) and filtered noises. Higher bicoherence values in chaotic time series and normal background EEG activities are indicative of nonlinear coupling in these systems. The paper shows utility of bispectral methods as an analytical tool in understanding neural process underlying human EEG patterns.

Bearing Fault Detection Using Higher-Order Statistics Based ARMA Model

2007 ◽  
Vol 347 ◽  
pp. 271-276 ◽  
Author(s):  
Fu Cai Li ◽  
Lin Ye ◽  
Gui Cai Zhang ◽  
Guang Meng
Keyword(s):  
Fault Detection ◽  
Order Statistics ◽  
Moving Average ◽  
Arma Model ◽  
Vibration Signal ◽  
Higher Order ◽  
Accurate Estimation ◽  
Higher Order Statistics ◽  
Identification Technique ◽  
Non Gaussian

Impulse response provides important information about flaws in mechanical system. Deconvolution is one system identification technique for fault detection when signals captured from bearings with and without flaw are both available. However effects of measurement systems and noise are obstacles to the technique. In the present study, a model, namely autoregressive-moving average (ARMA), is used to estimate vibration pattern of rolling element bearings for fault detection. The frequently used ARMA estimator cannot characterize non-Gaussian noise completely. Aimed at circumventing the inefficiency of the second-order statistics-based ARMA estimator, higher-order statistics (HOS) was introduced to ARMA estimator, which eliminates the effect of noise greatly and, therefore, offers more accurate estimation of the system. Furthermore, bispectrums of the estimated HOS-based ARMA models were subsequently applied to get clearer information. Impulse responses of signals captured from the test bearings without and with flaws and their bispectra were compared for the purpose of fault detection. The results demonstrated the excellent capability of this method in vibration signal processing and fault detection.

Experiment Study on Machinability of Six Kinds of Wrought Nickel-Based Superalloys

2006 ◽  
Vol 532-533 ◽  
pp. 444-447 ◽  
Author(s):  
Gang Liu ◽  
Ming Chen
Keyword(s):  
Tool Wear ◽  
Wear Mechanism ◽  
Manufacturing Industry ◽  
Failure Modes ◽  
Major Elements ◽  
Practical Significance ◽  
Nickel Based Superalloy ◽  
High Efficient ◽  
Tool Wear Mechanism ◽  
Cutting Vibration

The wrought nickel-based superalloy has been the indispensable material for aviation manufacturing industry, but it is also one of extremely difficult-to-cut materials. Now many researches were focused on the machinability of wrought nickel-based superalloy, and many useful and favorable results can be collected. But most of these researches studied on single kind of wrought nickel-based superalloy, the general and integrated study is absent. In this paper, six typical wrought nickel-based superalloys (GH80A, GH738, GH3030, GH3044, GH4033 and GH4169) were studied. By means of studies on tool wear rate, cutting forces, cutting vibration and tool wear mechanism, the comprehensive comparison of the machinability of wrought nickel-based superalloys was showed. The influences of major elements on the machinability were investigated. The machinability of the six kinds of wrought nickel-based superalloys queues from easiness to difficulty as follows: GH3030, GH3044, GH4033 and GH80A, GH4169, GH738. Finally the comprehensive comparisons of tool failure modes and wear mechanism of these wrought nickel-based superalloys were also presented. Experiment results are comprehensive and have great practical significance to the high efficient machining of wrought superalloys.

Milling Tool Wear Condition Monitoring Based on Extension Theory

2012 ◽  
Vol 562-564 ◽  
pp. 523-527
Author(s):  
Dong Min Wu ◽  
Ming Shen
Keyword(s):  
Tool Wear ◽  
Vibration Signal ◽  
Extension Theory ◽  
Milling Force ◽  
Condition Evaluation ◽  
Milling Tool ◽  
Force Signal ◽  
Ae Signal ◽  
Wear Monitoring ◽  
Wear Condition

The method of mill tool wear condition evaluation based on the extension theory is put forward by the paper. Through researching, the author firstly designs mill tool wear monitoring system which can acquire milling force signal, AE signal, vibration signal and main motor power signal. Based on the matter-element of classics, joint domain and evaluation, and deducting the dependent functions, at last the objective and reasonable evaluation results are got. It is proved that the extension theory is the valid and reliable in evaluating the mill tool wear condition.

On-site monitoring of bearing failure in composite bolted joints using built-in eddy current sensing film

2020 ◽  
pp. 002199832097973
Author(s):  
Qijian Liu ◽  
Hu Sun ◽  
Yuan Chai ◽  
Jianjian Zhu ◽  
Tao Wang ◽  
...  
Keyword(s):  
Numerical Simulations ◽  
Eddy Current ◽  
Failure Modes ◽  
Bolted Joints ◽  
Bearing Failure ◽  
Monitoring Method ◽  
Current Sensing ◽  
Site Monitoring ◽  
Array Sensing ◽  
Bearing Damage

Bearing damage is one of the common failure modes in composite bolted joints. This paper describes the development of an on-site monitoring method based on eddy current (EC) sensing film to monitor the bearing damage in carbon fiber reinforced plastic (CFRP) single-lap bolted joints under tensile testing. Configuration design and operating principles of EC array sensing film are demonstrated. A series of numerical simulations are conducted to analyze the variation of EC when the bearing failure occurs around the bolt hole. The results of damage detection in the horizontal direction and through the thickness direction in the bolt hole with different exciting current directions are presented by the finite element method (FEM). Experiments are performed to prove the feasibility of the proposed EC array sensing film when the bearing failure occurs in CFRP single-lap bolted joints. The results of numerical simulations and experiments indicate that bearing failure can be detected according to the variation of EC in the test specimen.

Investigation of microstructured milling tool for deferring tool wear

Wear ◽  
2011 ◽  
Vol 271 (9-10) ◽  
pp. 2433-2437 ◽  
Author(s):  
Wenlong Chang ◽  
Jining Sun ◽  
Xichun Luo ◽  
James M. Ritchie ◽  
Chris Mack
Keyword(s):  
Tool Wear ◽  
Milling Tool
Sign in / Sign up

Export Citation Format

Share Document