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.
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. 1–6. doi: 10.1109/ICMDCS.2017.8211718.
2.
MalikH.Wavelet and Hilbert Huang, Transform Based Wind Turbine Imbalance Fault Classification Model Using K-Nearest Neighbor Algorithm, Int J Renewable Energy Technology9(1/2) (2018), 66–83. doi: 10.1504/IJRET.2018.090105.
3.
MalikH., MishraS.Artificial Neural Network and Empirical Mode Decomposition Based Imbalance Fault Diagnosis of Wind Turbine Using TurbSim, FAST and Simulink, IET Renewable Power Generation11(6) (2017), 889–902. Doi: 10.1049/iet-rpg.2015.0382.
4.
MalikH. and SharmaR., EMD and ANN based Intelligent Fault Diagnosis Model for Transmission Line, Journal of Intelligent & Fuzzy Systems32(4) (2017), 3043–3050. Doi: 10.3233/JIFS-169247.
5.
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), 1–13Doi: 10.1080/15325008.2014.956952.
6.
MalikH. and MishraS., Application of GEP to Investigate the Imbalance Faults in Direct-Drive Wind Turbine Using Generator Current Signals, IET Renewable Power Generation11(6) (2017), 889–902. doi: 10.1049/iet-rpg.2016.0689.
7.
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.
8.
MalikH. and MishraS., Application of ProbabilisticNeural Network in Fault Diagnosis of Wind Turbine Using FAST, TurbSim and Simulink, in Elsevier Procedia Computer Science58 (2015), 186–193. Doi: 10.1016/j.procs.2015.08.052
9.
MalikH., MishraS., Proximal Support Vector machine (PSVM) Based Imbalance Fault Diagnosis of Wind Turbine Using Generator Current Signals, in Elsevier Energy Pro-cedia90 (2015), 593–603, 15-17, IIT Bombay, India. Doi: 10.1016/j.egypro.2016.11.228.
10.
YadavA.K., MalikH. and ChandelS.S., Selection of Most Relevant Input Parameters Using WEKA for Artificial Neural Network Based Solar Radiation Prediction Models, Renewable and Sustainable Energy Reviews31 (2014), 509–519Doi: 10.1016/j.rser.2013.12.008.
11.
SharmaR., AggarwalA. and MalikH., Selection of most relevant input parameters using WEKA for artificial neural network based transmission line fault diagnosis model, in Proc of the Intl Conf on Nanotechnology for Better Living3(1) (2016), 176. Doi: 10.3850/978-981-09-7519-7nbl16-rps-176.
12.
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. 1–6, 25-27. DOI: 10.1109/POWERI.2016.8077368.
13.
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.
14.
MalikH. and MishraS., Application of Probabilistic Neural Network in Fault Diagnosis of Wind Turbine Using FAST, TurbSim and Simulink, in Elsevier Procedia Computer Science58 (2015), 186–193. Doi: 10.1016/j.procs.2015.08.052.
15.
MalikH., KhatriA. and DohareR., Probabilistic Neural Network Based Incipient Fault Identification Using DGA Dataset, in Elsevier Procedia Computer Science58 (2015), 665–672. Doi: 10.1016/j.procs.2015.08.086.
16.
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), 1–6. DOI: 10.1109/ICPEICES.2016.7853220.
17.
KumarG. and MalikH., Generalized Regression Neural Network Based Wind Speed Prediction Model For Western Region Of India, in Elsevier Procedia Computer Science93 (2016), 26–32. Doi: 10.1016/j.procs.2016.07.177.
18.
YadavA.K., MalikH. and ChandelS.S., Application of Rapid Miner In ANN Based Prediction Of Solar Radiation for Assessment of Solar Energy Resource Potential of 76 Sites in Northwestern India, Renewable and Sustainable Energy Reviews52 (2015), 1093–1106. Doi: 10.1016/j.rser.2015.07.156.
19.
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), 474–480. Doi: 10.1049/cp.2015.1679.
20.
KumarG., SharmaS. and MalikH., Learning Vector Quantization Neural Network Based External Fault Diagnosis Model for Three Phase Induction Motor Using Current Signature Analysis, in Elsevier Procedia Computer Science93 (2016), 1010–1016. Doi: 10.1016/j.procs.2016.07.304.
