{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T16:28:50Z","timestamp":1726849730737},"reference-count":48,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,10]],"date-time":"2018-11-10T00:00:00Z","timestamp":1541808000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Tool fault diagnosis in numerical control (NC) machines plays a significant role in ensuring manufacturing quality. Tool condition monitoring (TCM) based on multisensors can provide more information related to tool condition, but it can also increase the risk that effective information is overwhelmed by redundant information. Thus, the method of obtaining the most effective feature information from multisensor signals is currently a hot topic. However, most of the current feature selection methods take into account the correlation between the feature parameters and the tool state and do not analyze the influence of feature parameters on prediction accuracy. In this paper, a multisensor global feature extraction method for TCM in the milling process is researched. Several statistical parameters in the time, frequency, and time\u2013frequency (Wavelet packet transform) domains of multiple sensors are selected as an alternative parameter set. The monitoring model is executed by a Kernel-based extreme learning Machine (KELM), and a modified genetic algorithm (GA) is applied in order to search the optimal parameter combinations in a two-objective optimization model to achieve the highest prediction precision. The experimental results show that the proposed method outperforms the Pearson\u2019s correlation coefficient (PCC) based, minimal redundancy and maximal relevance (mRMR) based, and Principal component analysis (PCA)-based feature selection methods.<\/jats:p>","DOI":"10.3390\/s18113866","type":"journal-article","created":{"date-parts":[[2018,11,14]],"date-time":"2018-11-14T07:42:41Z","timestamp":1542181361000},"page":"3866","source":"Crossref","is-referenced-by-count":69,"title":["A Multisensor Fusion Method for Tool Condition Monitoring in Milling"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-8580-5427","authenticated-orcid":false,"given":"Yuqing","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China"},{"name":"College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China"}]},{"given":"Wei","family":"Xue","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Javed, K., Gouriveau, R., Li, X., and Zerhouni, N. (2016). Tool wear monitoring and prognostics challenges: A comparison of connectionist methods toward an adaptive ensemble model. J. Intell. Manuf., 1\u201318.","DOI":"10.1007\/s10845-016-1221-2"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3061","DOI":"10.1177\/1077546314520835","article-title":"An investigation of tool wear using acoustic emission and genetic algorithm","volume":"21","author":"Vetrichelvan","year":"2014","journal-title":"J. Vib. Control"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2665","DOI":"10.1016\/j.ymssp.2007.01.004","article-title":"Cutting force-based real-time estimation of tool wear in face milling using a combination of signal processing techniques","volume":"21","author":"Bhattacharyya","year":"2007","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.asoc.2015.06.023","article-title":"Incremental learning for online tool condition monitoring using ellipsoid artmap network model","volume":"35","author":"Liu","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.ymssp.2008.02.010","article-title":"Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system","volume":"23","author":"Aliustaoglu","year":"2009","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1007\/s00170-013-5560-2","article-title":"Reliability assessment of cutting tool life based on surrogate approximation methods","volume":"71","author":"Konstantinos","year":"2014","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.1007\/s00170-014-6560-6","article-title":"Tool wear monitoring using na\u00efve bayes classifiers","volume":"77","author":"Karandikar","year":"2015","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1016\/j.cirp.2015.05.011","article-title":"Cloud-enabled prognosis for manufacturing","volume":"64","author":"Gao","year":"2015","journal-title":"CIRP Ann."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s00170-004-2038-2","article-title":"State-of-the-art methods and results in tool condition monitoring: A review","volume":"26","author":"Rehorn","year":"2005","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4249","DOI":"10.1016\/j.measurement.2013.07.015","article-title":"Correlation study of tool flank wear with machined surface texture in end milling","volume":"46","author":"Dutta","year":"2013","journal-title":"Measurement"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.ymssp.2005.10.010","article-title":"Estimation of tool wear during CNC milling using neural network-based sensor fusion","volume":"21","author":"Ghosh","year":"2007","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.jmapro.2016.03.010","article-title":"Tool life predictions in milling using spindle power with the neural network technique","volume":"22","author":"Drouillet","year":"2016","journal-title":"J. Manuf. Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1115\/1.2899768","article-title":"Sensor Synthesis for Control of Manufacturing Processes","volume":"114","author":"Chryssolouris","year":"1992","journal-title":"J. Eng. Ind. ASME"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ijmachtools.2014.10.011","article-title":"Real-time tool wear monitoring in milling using a cutting condition independent method","volume":"89","author":"Nouri","year":"2015","journal-title":"Int. J. Mach. Tools Manuf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.advengsoft.2014.12.010","article-title":"Monitoring of tool wear using measured machining forces and neuro-fuzzy modelling approaches during machining of gfrp composites","volume":"82","author":"Azmi","year":"2015","journal-title":"Adv. Eng. Softw."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1177\/0954405414526578","article-title":"Cutting forces and tool failure in high-speed milling of titanium alloy tc21 with coated carbide tools","volume":"229","author":"Zhang","year":"2015","journal-title":"Proc. Inst. Mech. Eng. Part B J. Eng. Manuf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1007\/s00170-015-7317-6","article-title":"Tool wear predictability estimation in milling based on multi-sensorial data","volume":"82","author":"Stavropoulos","year":"2016","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1016\/S0890-6955(99)00122-4","article-title":"Sensor signals for tool-wear monitoring in metal cutting operations\u2014A review of methods","volume":"40","author":"Snr","year":"2000","journal-title":"Int. J. Mach. Tools Manuf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2083","DOI":"10.19026\/rjaset.7.502","article-title":"A review of sensor system and application in milling process for tool condition monitoring","volume":"7","author":"Rizal","year":"2014","journal-title":"Res. J. Appl. Sci. Eng. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/S0890-6955(99)00066-8","article-title":"Data fusion neural network for tool condition monitoring in cnc milling machining","volume":"40","author":"Chen","year":"2000","journal-title":"Int. J. Mach. Tools Manuf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.1007\/s00170-018-1768-5","article-title":"Review of tool condition monitoring methods in milling processes","volume":"96","author":"Zhou","year":"2018","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, C., Yao, X., Zhang, J., and Jin, H. (2016). Tool condition monitoring and remaining useful life prognostic based on a wireless sensor in dry milling operations. Sensors, 16.","DOI":"10.3390\/s16060795"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/10910340601174806","article-title":"Methods for on-line directionally independent failure prediction of end milling cutting tools","volume":"11","author":"Suprock","year":"2007","journal-title":"Mach. Sci. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.asoc.2015.08.019","article-title":"A PNN self-learning tool breakage detection system in end milling operations","volume":"37","author":"Huang","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.rcim.2016.12.009","article-title":"Fuzzy logic based tool condition monitoring for end-milling","volume":"47","author":"Cuka","year":"2017","journal-title":"Robot. Comput.-Integrated Manuf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1016\/j.ijmachtools.2009.02.003","article-title":"Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results","volume":"49","author":"Zhu","year":"2009","journal-title":"Int. J. Mach. Tools Manuf."},{"key":"ref_27","first-page":"1","article-title":"Face milling tool condition monitoring using sound signal","volume":"2017","author":"Madhusudana","year":"2017","journal-title":"Int. J. Syst. Assurance Eng. Manag."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.measurement.2014.12.037","article-title":"FPGA-based reconfigurable system for tool condition monitoring in high-speed machining process","volume":"64","year":"2015","journal-title":"Measurement"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, C., Li, Y., Zhou, G., and Shen, W. (2016). A sensor fusion and support vector machine based approach for recognition of complex machining conditions. J. Intell. Manuf., 1\u201314.","DOI":"10.1007\/s10845-016-1209-y"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1007\/s00170-011-3536-7","article-title":"CHMM for tool condition monitoring and remaining useful life prediction","volume":"59","author":"Wang","year":"2012","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.procir.2013.09.012","article-title":"An Adaptive SPC Approach for Multi-sensor Fusion and Monitoring of Time-varying Processes","volume":"12","author":"Grasso","year":"2013","journal-title":"Procedia Cirp"},{"key":"ref_32","first-page":"1","article-title":"A new tool wear monitoring method based on multi-scale pca","volume":"7","author":"Wang","year":"2016","journal-title":"J. Intell. Manuf."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.sna.2014.01.004","article-title":"Vibration sensor based tool condition monitoring using \u03bd, support vector machine and locality preserving projection","volume":"209","author":"Wang","year":"2014","journal-title":"Sens. Actuators A Phys."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.rcim.2016.05.010","article-title":"Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing","volume":"45","author":"Wang","year":"2017","journal-title":"Robot. Comput. Integr. Manuf."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"21588","DOI":"10.3390\/s141121588","article-title":"Force sensor based tool condition monitoring using a heterogeneous ensemble learning model","volume":"14","author":"Wang","year":"2014","journal-title":"Sensors"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1007\/s00170-009-2110-z","article-title":"Design of multisensor fusion-based tool condition monitoring system in end milling","volume":"46","author":"Cho","year":"2010","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","article-title":"Extreme learning machine for regression and multiclass classification","volume":"42","author":"Huang","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1007\/s12559-015-9333-0","article-title":"What are Extreme Learning Machines? Filling the Gap Between Frank Rosenblatt\u2019s Dream and John von Neumann\u2019s Puzzle","volume":"7","author":"Huang","year":"2015","journal-title":"Cognit. Comput."},{"key":"ref_39","first-page":"83","article-title":"Overview of Deep Kernel Learning Based Techniques and Applications","volume":"1","author":"Chen","year":"2016","journal-title":"J. Netw. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.inffus.2005.01.001","article-title":"Multi-sensor fusion: An Evolutionary algorithm approach","volume":"7","author":"Maslov","year":"2006","journal-title":"Inf. Fusion"},{"key":"ref_41","unstructured":"(2018, June 01). The Prognostics and Health Management Society, 2010 Conference Data Challenge. Available online: https:\/\/www.phmsociety.org\/competition\/phm\/10."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3311","DOI":"10.1007\/s00170-018-2018-6","article-title":"Tool wear condition monitoring based on continuous wavelet transform and blind source separation","volume":"97","author":"Benkedjouh","year":"2018","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2925","DOI":"10.1177\/1077546314545097","article-title":"An online damage identification approach for numerical control machine tools based on data fusion using vibration signals","volume":"21","author":"Zhou","year":"2015","journal-title":"J. Vib. Control"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhao, R., Yan, R., Wang, J., and Mao, K. (2017). Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks. Sensors, 17.","DOI":"10.3390\/s17020273"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Gao, C., Xue, W., Ren, Y., and Zhou, Y. (2017). Numerical control machine tool fault diagnosis using hybrid stationary subspace analysis and least squares support vector machine with a single sensor. Appl. Sci., 7.","DOI":"10.3390\/app7040346"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.1109\/TIE.2017.2733438","article-title":"Machine Health Monitoring Using Local Feature-based Gated Recurrent Unit Networks","volume":"65","author":"Zhao","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_47","first-page":"2526","article-title":"Reliability estimation for cutting tools based on logistic regression model using vibration signals","volume":"25","author":"Chen","year":"2011","journal-title":"Noise Vib. Bull."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wang, G., and Grosu, R. (2017). Milling-tool wear-condition prediction with statistic analysis and echo-state networks. Challenges Technol. Innov., 149\u2013153.","DOI":"10.1201\/9781315198101-27"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3866\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,13]],"date-time":"2024-06-13T13:04:18Z","timestamp":1718283858000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3866"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,10]]},"references-count":48,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["s18113866"],"URL":"https:\/\/doi.org\/10.3390\/s18113866","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,10]]}}}