Combining translation-invariant wavelet frames and convolutional neural network for intelligent tool wear state identification

Abstract On-machine monitoring of tool wear in machining processes has found its importance to reduce equipment downtime and reduce tooling costs. As the tool wears out gradually, the contact state of the cutting edge and the workpiece changes, which has a significant influence on the vibration state of the spindle. The performance of traditional intelligent fault diagnosis methods depend on feature extraction of dynamic signals, which requires expert knowledge and human labor. Recently, deep learning algorithms have been applied widely in machine health monitoring. In this paper, we present a novel intelligent technique for tool wear state recognition using machine spindle vibration signals. The proposed technique combines derived wavelet frames (DWFs) and convolutional neural network (CNN). Constructed based on dual tree wavelets, DWF are equipped with merits of centralized multiresolution and nearly translation-invariance. In this method, DWFs are employed to decompose the original signal into frequency bands of different bandwidths and different center frequencies, which are more pronounced as the tool wears. Further, the reconstructed sub-signals are stacked into a 2-D signal matrix to match the structure of 2-D CNN while retaining more dynamic information. The 2-D convolutional neural network is utilized to automatically recognize features from the multiscale 2-D signal matrix. End-milling experiments were performed on a S45C steel workpiece at different machining parameters. The experiment results of the recognition for tool wear state show the feasibility and effectiveness of the proposed method.

[1]  Li Dan,et al.  Tool wear and failure monitoring techniques for turning—A review , 1990 .

[2]  Ruqiang Yan,et al.  Weighted low-rank sparse model via nuclear norm minimization for bearing fault detection , 2017 .

[3]  Fulei Chu,et al.  HVSRMS localization formula and localization law: Localization diagnosis of a ball bearing outer ring fault , 2019, Mechanical Systems and Signal Processing.

[4]  Dimitri Palaz,et al.  Estimating phoneme class conditional probabilities from raw speech signal using convolutional neural networks , 2013, INTERSPEECH.

[5]  Shuang Liang,et al.  A weighted hidden Markov model approach for continuous-state tool wear monitoring and tool life prediction , 2016, The International Journal of Advanced Manufacturing Technology.

[6]  Eduardo Carlos Bianchi,et al.  Evaluation of neural models applied to the estimation of tool wear in the grinding of advanced ceramics , 2015, Expert Syst. Appl..

[7]  Jean Ponce,et al.  A Theoretical Analysis of Feature Pooling in Visual Recognition , 2010, ICML.

[8]  Steven Verstockt,et al.  Convolutional Neural Network Based Fault Detection for Rotating Machinery , 2016 .

[9]  Shuilong He,et al.  Wavelet Transform Based on Inner Product for Fault Diagnosis of Rotating Machinery , 2017 .

[10]  Ruqiang Yan,et al.  Improving calibration accuracy of a vibration sensor through a closed loop measurement system , 2016, IEEE Instrumentation & Measurement Magazine.

[11]  Shuhui Wang,et al.  Convolutional neural network-based hidden Markov models for rolling element bearing fault identification , 2017, Knowl. Based Syst..

[12]  Furong Gao,et al.  Review of Recent Research on Data-Based Process Monitoring , 2013 .

[13]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[14]  Guillem Quintana,et al.  Chatter in machining processes: A review , 2011 .

[15]  Ming Yang,et al.  3D Convolutional Neural Networks for Human Action Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Dong-Won Kim,et al.  Fuzzy logic based tool condition monitoring for end-milling , 2017 .

[17]  Thomas R. Kurfess,et al.  Study of spindle power data with neural network for predicting real-time tool wear/breakage during inconel drilling , 2017 .

[18]  Asoke K. Nandi,et al.  Genetic algorithms for feature selection in machine condition monitoring with vibration signals , 2000 .

[19]  Richard Baraniuk,et al.  The Dual-tree Complex Wavelet Transform , 2007 .

[20]  László Monostori,et al.  Artificial neural network based tool condition monitoring in micro mechanical peck drilling using thrust force signals , 2017 .

[21]  Sam Turner,et al.  Tool wear monitoring using naïve Bayes classifiers , 2014, The International Journal of Advanced Manufacturing Technology.

[22]  Hossein Hakim,et al.  A comparative study of non-parametric spectral estimators for application in machine vibration analysis , 1992 .

[23]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

[24]  Wenyu Liu,et al.  Traffic sign detection and recognition using fully convolutional network guided proposals , 2016, Neurocomputing.

[25]  Dragos Axinte,et al.  A critical analysis of effectiveness of acoustic emission signals to detect tool and workpiece malfunctions in milling operations , 2008 .

[26]  Shih-Chieh Lin,et al.  Tool wear monitoring in face milling using force signals , 1996 .

[27]  Ruqiang Yan,et al.  A sparse auto-encoder-based deep neural network approach for induction motor faults classification , 2016 .

[28]  Hongkai Jiang,et al.  An adaptive deep convolutional neural network for rolling bearing fault diagnosis , 2017 .

[29]  Surjya K. Pal,et al.  Tool condition classification in turning process using hidden Markov model based on texture analysis of machined surface images , 2016 .

[30]  Yanyang Zi,et al.  Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform , 2010 .

[31]  Colin Bradley,et al.  A review of machine vision sensors for tool condition monitoring , 1997 .

[32]  Zsolt János Viharos,et al.  Support Vector Machine (SVM) based general model building algorithm for production control , 2011 .

[33]  Liang Gao,et al.  A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method , 2018, IEEE Transactions on Industrial Electronics.

[34]  Mika Lohtander,et al.  Tool condition monitoring in interrupted cutting with acceleration sensors , 2017 .

[35]  Peng Wang,et al.  An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox , 2017, Sensors.

[36]  Durul Ulutan,et al.  In-Process Tool Flank Wear Estimation in Machining Gamma-Prime Strengthened Alloys Using Kalman Filter , 2015 .

[37]  Surjya K. Pal,et al.  On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression , 2016 .

[38]  Amin Al-Habaibeh,et al.  A new approach for systematic design of condition monitoring systems for milling processes , 2000 .

[39]  H. Metin Ertunç,et al.  Tool Wear Condition Monitoring in Drilling Processes Using Fuzzy Logic , 2006, ICONIP.

[40]  Dongfeng Shi,et al.  Tool wear predictive model based on least squares support vector machines , 2007 .

[41]  Yuan Gao,et al.  Machining vibration states monitoring based on image representation using convolutional neural networks , 2017, Eng. Appl. Artif. Intell..

[42]  Krzysztof Jemielniak,et al.  Advanced monitoring of machining operations , 2010 .

[43]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[44]  Pan Fu,et al.  Intelligent Tool Condition Monitoring in Milling Operation , 1998 .

[45]  A. I. Azmi,et al.  Monitoring of tool wear using measured machining forces and neuro-fuzzy modelling approaches during machining of GFRP composites , 2015, Adv. Eng. Softw..

[46]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[47]  Hee-Jun Kang,et al.  Rolling element bearing fault diagnosis using convolutional neural network and vibration image , 2019, Cognitive Systems Research.

[48]  Farbod Akhavan Niaki,et al.  State of health monitoring in machining: Extended Kalman filter for tool wear assessment in turning of IN718 hard-to-machine alloy , 2016 .

[49]  Lianwen Jin,et al.  DropSample: A New Training Method to Enhance Deep Convolutional Neural Networks for Large-Scale Unconstrained Handwritten Chinese Character Recognition , 2015, Pattern Recognit..

[50]  Chen Binqian,et al.  Novel Ensemble Analytic Discrete Framelet Expansion for Machinery Fault Diagnosis , 2014 .

[51]  T. Kurfess,et al.  Tool life predictions in milling using spindle power with the neural network technique , 2016 .

[52]  Binqiang Chen,et al.  Complex wavelet enhanced shape from shading transform for estimating surface roughness of milled mechanical components , 2017 .

[53]  Binqiang Chen,et al.  An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network , 2017, Materials.

[54]  Ming-Chyuan Lu,et al.  Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling , 2011, The International Journal of Advanced Manufacturing Technology.

[55]  Robert X. Gao,et al.  Wavelets for fault diagnosis of rotary machines: A review with applications , 2014, Signal Process..