{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:49:15Z","timestamp":1777704555970,"version":"3.51.4"},"reference-count":20,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2018,8,16]],"date-time":"2018-08-16T00:00:00Z","timestamp":1534377600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2018,11,20]]},"abstract":"<jats:p>Rolling bearing is an important mechanical element therefore its condition monitoring is necessary to ensure the steadiness of industrial machineries. In this paper, the open source vibration data have been processed using advanced signal processing techniques such as EMD method to extract more symmetric waves (IMFs) out of non-linear and non-stationary vibration signals. In addition to this, statistical time-domain and frequency-domain features are calculated and then J48 Decision Tree Algorithm is used for feature selection. The processed input signals have been used for comparative study of five different types of Artificial Neural Network (ANN) classifiers. The performance characteristics of MLP, PNN, GRNN, RBF and LVQ are shown in results and discussion section.<\/jats:p>","DOI":"10.3233\/jifs-169821","type":"journal-article","created":{"date-parts":[[2018,8,17]],"date-time":"2018-08-17T11:20:41Z","timestamp":1534504841000},"page":"5391-5402","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":35,"title":["EMD and ANN based intelligent model for bearing fault diagnosis"],"prefix":"10.1177","volume":"35","author":[{"given":"Arjun Kumar","family":"Shah","sequence":"first","affiliation":[{"name":"Division of Manufacturing Process and Automation Engineering, NSIT, New Delhi, India"}]},{"given":"Ashish","family":"Yadav","sequence":"additional","affiliation":[{"name":"Division of Manufacturing Process and Automation Engineering, NSIT, New Delhi, India"}]},{"given":"H.","family":"Malik","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, IIT Delhi, New Delhi, India"}]}],"member":"179","published-online":{"date-parts":[[2018,8,16]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","unstructured":"SinghK.V. SharmaR. MAlikH.Condition monitoring of wind turbine gearbox using electrical signatures in IEEE Proc Int Conf on Microelectronic Devices Circuits and Systems (ICMDCS) (2017) pp. 1\u20136. doi: 10.1109\/ICMDCS.2017.8211718.","DOI":"10.1109\/ICMDCS.2017.8211718"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1504\/IJRET.2018.090105"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-rpg.2015.0382"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-169247"},{"issue":"16","key":"e_1_3_1_6_2","first-page":"1","article-title":"Selection of Most Relevant Input Parameters Using Waikato Environment for Knowledge Analysis for Gene Expression Programming Based Power Transformer Fault Diagnosis","volume":"42","author":"Malik H.","year":"2014","unstructured":"MalikH., MishraS. and MittalA.P., Selection of Most Relevant Input Parameters Using Waikato Environment for Knowledge Analysis for Gene Expression Programming Based Power Transformer Fault Diagnosis, International Journal of Electric Power Components and Systems42(16) (2014), 1\u201313Doi: 10.1080\/15325008.2014.956952.","journal-title":"International Journal of Electric Power Components and Systems"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-rpg.2015.0382"},{"key":"e_1_3_1_8_2","unstructured":"AliJ.B. FnaiechN. SaidiL. Chebel-MorelloB.Farhat Fnaiech Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals."},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.08.052"},{"key":"e_1_3_1_10_2","first-page":"593","volume-title":"in Elsevier Energy Pro-cedia","author":"Malik H.","year":"2015","unstructured":"MalikH., MishraS., Proximal Support Vector machine (PSVM) Based Imbalance Fault Diagnosis of Wind Turbine Using Generator Current Signals, in Elsevier Energy Pro-cedia90 (2015), 593\u2013603, 15-17, IIT Bombay, India. Doi: 10.1016\/j.egypro.2016.11.228."},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2013.12.008"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.3850\/978-981-09-7519-7nbl16-rps-176"},{"key":"e_1_3_1_13_2","first-page":"1","article-title":"Selection of Most Relevant Input Parameters Using WEKA for Artificial Neural Network Based Concrete Compressive Strength Prediction Model","author":"Saad S.","year":"2016","unstructured":"SaadS. and MalikH., Selection of Most Relevant Input Parameters Using WEKA for Artificial Neural Network Based Concrete Compressive Strength Prediction Model, in Proc IEEE PIICON-2016 (2016), Pp. 1\u20136, 25-27. DOI: 10.1109\/POWERI.2016.8077368.","journal-title":"in Proc IEEE PIICON-2016"},{"key":"e_1_3_1_14_2","doi-asserted-by":"crossref","unstructured":"MalikH. and MishraS. Feature Selection using Rapid-Miner and Classification through Probabilistic Neural Network for Fault Diagnostics of Power Transformer in Proc IEEE Int Conf on Emerging Trends and Innovation in Technology (INDICON 2014) 11-13 Dec. 2014 Pune India. DOI: 10.1109\/INDICON.2014.7030427.","DOI":"10.1109\/INDICON.2014.7030427"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.08.052"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.08.086"},{"key":"e_1_3_1_17_2","first-page":"1","article-title":"Wind Speed and Power Prediction of Prominent Wind Power Potential States in India using GRNN","author":"Savita","year":"2016","unstructured":"Savita, , AnsariM.A., Pal, N.S. and MalikH., Wind Speed and Power Prediction of Prominent Wind Power Potential States in India using GRNNin Proc IEEE ICPEICES-2016 (2016), 1\u20136. DOI: 10.1109\/ICPEICES.2016.7853220.","journal-title":"in Proc IEEE ICPEICES-2016"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2016.07.177"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2015.07.156"},{"key":"e_1_3_1_20_2","doi-asserted-by":"crossref","unstructured":"MalikH. and MishraS. Application of LVQ Network In Fault Diagnosis Of Wind Turbine Using TurbSim FAST and Simulink in Michael Faraday IET International Summit 2015 (2015) 474\u2013480. Doi: 10.1049\/cp.2015.1679.","DOI":"10.1049\/cp.2015.1679"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2016.07.304"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-169821","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-169821","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-169821","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:41:28Z","timestamp":1777455688000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-169821"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,16]]},"references-count":20,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2018,11,20]]}},"alternative-id":["10.3233\/JIFS-169821"],"URL":"https:\/\/doi.org\/10.3233\/jifs-169821","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8,16]]}}}