Tool Wear Detection Based on Wavelet Packet and BP Neural Network

2010 ◽  
Author(s):  
Yuxia Qin ◽  
Lanshen Guo ◽  
Jian Wang
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Bp Neural Network ◽  
Wavelet Packet

scholarly journals Discrete wavelet transformation and genetic algorithm – back propagation neural network applied in monitoring woodworking tool wear conditions in the milling operation spindle power signals

BioResources ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. 2369-2384
Author(s):  
Weihang Dong ◽  
Xiaolei Guo ◽  
Yong Hu ◽  
Jinxin Wang ◽  
Guangjun Tian
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Discrete Wavelet ◽  
Feature Extraction Method ◽  
Monitoring Model ◽  
Plastic Composite ◽  
Spindle Power

Tool wear conditions monitoring is an important mechanical processing system that can improve the processing quality of wood plastic composite furniture and reduce industrial energy consumption. An appropriate signal, feature extraction method, and model establishment method can effectively improve the accuracy of tool wear monitoring. In this work, an effective method based on discrete wavelet transformation (DWT) and genetic algorithm (GA) – back propagation (BP) neural network was proposed to monitor the tool wear conditions. The spindle power signals under different spindle speeds, depths of milling, and tool wear conditions were collected by power sensors connected to the machine tool control box. Based on the feature extraction method, the approximate coefficients of spindle power signal were extracted by DWT. Then, the extracted approximate coefficients, spindle speeds, depths of milling, and tool wear conditions were taken as samples to train the monitoring model. Threshold and weight of BP neural network were optimized by GA, and the accuracy of monitoring model established by the GA – BP neural network can reach 100%. Thus, the proposed monitoring method can accurately monitor tool wear conditions with different milling parameters, which can achieve the purpose of improving the processing quality of wood plastic composite furniture and reducing energy consumption.

Fault Diagnosis of Large Rotors Based on Wavelet Packet Neural Network

2014 ◽  
Vol 722 ◽  
pp. 363-366
Author(s):  
You Juan Zheng ◽  
Ping Liao ◽  
Cai Long Qin ◽  
Yu Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Test Bench ◽  
Feature Vector ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Network Method ◽  
Spectrum Characteristic ◽  
Fault Information

Using wavelet packet neural network method which is consist of wavelet packet and BP neural network to diagnose large rotors by vibration signal .Firstly , according to the spectrum characteristic of large rotors’ common vibration fault ,using the improved wavelet packet method to compute the energy of the spectrum that can reflect the fault information .And then make the feature vector as the input to establish a model of improved wavelet packet neural network for fault diagnosis . Collect the data of five working conditions from the test bench , establish a improved wavelet packet neural network model, and then use the model to diagnose fault. The experimental results show that this method improves the accuracy obviously and calculate fast.

Multisensor Information Fusion Based on D-S Evidence Theory and BP Neural Network

2013 ◽  
Vol 567 ◽  
pp. 113-117 ◽  
Author(s):  
Can Zhao ◽  
C.R. Tang ◽  
S. Wan
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Cutting Force ◽  
Information Fusion ◽  
Bp Neural Network ◽  
Cutting Tool ◽  
Evidence Theory ◽  
Vibration Signal ◽  
Inspection System ◽  
Tool Monitoring

This paper applies the information fusion technology to tool monitoring. As one of the most important processing factor, the cutting tool and the tool wear directly influence size precision. Signals of cutting force and vibration are measured with multi-sensor. By using multi-sensor the drawbacks can be overcome, the multi-sensor information fusion mentioned in manufacture stands for extracting kinds of information from different sensors (especially for cutting force and vibration signal in this paper), making best use of all resources，according to certain criterion to judge the spatial and time redundancy , to make the system more comprehensive. Two data fusion methods, which are BP Neural Network and Wavelet Neural Network for predicting tool wear, and are debated. By the hybrid of BP and wavelet based neural network the cutting tool status inspection system is built so that the forecast of tool wear can be achieved. The results show experimentally two of these presented methods effectively implement tool wear monitoring and predicting.

Gear Fault Diagnosis Based on Harmonic Wavelet Packet and BP Neural Network

2012 ◽  
Vol 217-219 ◽  
pp. 2683-2687 ◽  
Author(s):  
Chen Jiang ◽  
Xue Tao Weng ◽  
Jing Jun Lou
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Wavelet Packet ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Fault Detection And Identification ◽  
Gear Fault ◽  
Harmonic Wavelet ◽  
Gear Fault Diagnosis

The gear fault diagnosis system is proposed based on harmonic wavelet packet transform (WPT) and BP neural network techniques. The WPT is a well-known signal processing technique for fault detection and identification in mechanical system，In the preprocessing of vibration signals, WPT coefficients are used for evaluating their energy and treated as the features to distinguish the fault conditions.In the experimental work, the harmonic wavelets are used as mother wavelets to build and perform the proposed WPT technique. The experimental results showed that the proposed system achieved an average classification accuracy of over 95% for various gear working conditions.

Identification of Tool Wear Condition Based on Generalized Fractal Dimensions and BP Neural Network Optimized with Genetic Algorithm

2013 ◽  
Vol 318 ◽  
pp. 71-75 ◽  
Author(s):  
Kai Feng Zhang ◽  
Hui Qun Yuan ◽  
Peng Nie
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Tool Wear ◽  
Bp Neural Network ◽  
Fractal Theory ◽  
Fractal Dimensions ◽  
Cutting Process ◽  
Generalized Dimensions ◽  
Ae Signals ◽  
Wear Condition

Based on multi-fractal theory, the generalized fractal dimensions of acoustic emission (AE) signals during cutting process were calculated using improved box-counting method. The generalized dimension spectrums of AE signals for different tool wear condition were gained, and the relation between tool wear condition and generalized dimensions was analyzed. Together with cutting process parameters, the generalized fractal dimensions were taken as the input vectors of BP neural network after normalization. The initial weight and bias values of BP neural network which was used to classify the tool wear condition were optimized with Genetic Algorithm. The test results showed that the method can be used effectively for the identification of tool wear condition.

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