{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T05:17:33Z","timestamp":1725945453133},"reference-count":30,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,23]],"date-time":"2021-07-23T00:00:00Z","timestamp":1626998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013129","name":"Ministry of SMEs and Startups","doi-asserted-by":"publisher","award":["S3126818"],"id":[{"id":"10.13039\/501100013129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"In this research, the aim is to investigate an adaptive digital twin algorithm for fault diagnosis and crack size identification in bearings. The main contribution of this research is to design an adaptive digital twin (ADT). The design of the ADT technique is based on two principles: normal signal modeling and estimation of signals. A combination of mathematical and data-driven techniques will be used to model the normal vibration signal. Therefore, in the first step, the normal vibration signal is modeled to increase the reliability of the modeling algorithm in the ADT. Then, to help challenge the complexity and uncertainty, the data-driven method will solve the problems of the mathematically based algorithm. Thus, first, Gaussian process regression is selected, and then, in two steps, we improve its resistance and accuracy by a Laguerre filter and fuzzy logic algorithm. After modeling the vibration signal, the second step is to design the data estimation for ADT. These signals are estimated by an adaptive observer. Therefore, a proportional-integral observer is then combined with the proposed technique for signal modeling. Then, in two stages, its robustness and reliability are strengthened using the Lyapunov-based algorithm and adaptive technique, respectively. After designing the ADT, the residual signals that are the difference between original and estimated signals are obtained. After that, the residual signals are resampled, and the root means square (RMS) signals are extracted from the residual signals. A support vector machine (SVM) is recommended for fault classification and crack size identification. The strength of the proposed technique is tested using the Case Western Reserve University Bearing Dataset (CWRUBD) under diverse torque loads, various motor speeds, and different crack sizes. In terms of fault diagnosis, the average detection accuracy in the proposed scheme is 95.75%. In terms of crack size identification for the roller, inner, and outer faults, the proposed scheme has average detection accuracies of 97.33%, 98.33%, and 98.33%, respectively.<\/jats:p>","DOI":"10.3390\/s21155009","type":"journal-article","created":{"date-parts":[[2021,7,23]],"date-time":"2021-07-23T14:31:44Z","timestamp":1627050704000},"page":"5009","source":"Crossref","is-referenced-by-count":16,"title":["Crack Size Identification for Bearings Using an Adaptive Digital Twin"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-8420-9500","authenticated-orcid":false,"given":"Farzin","family":"Piltan","sequence":"first","affiliation":[{"name":"Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Korea"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5185-1062","authenticated-orcid":false,"given":"Jong-Myon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106556","DOI":"10.1016\/j.ymssp.2019.106556","article-title":"Blind filters based on envelope spectrum sparsity indicators for bearing and gear vibration-based condition monitoring","volume":"138","author":"Peeters","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107773","DOI":"10.1016\/j.measurement.2020.107773","article-title":"A survey of non-destructive techniques used for inspection of bearing steel balls","volume":"159","author":"Zhang","year":"2020","journal-title":"Measurement"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2020\/8843759","article-title":"A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor","volume":"2020","author":"Alshorman","year":"2020","journal-title":"Shock. Vib."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"107050","DOI":"10.1016\/j.ymssp.2020.107050","article-title":"High-speed train wheel set bearing fault diagnosis and prognostics: A new prognostic model based on extendable useful life","volume":"146","author":"Xu","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1080\/17415977.2018.1475479","article-title":"Strain analysis by a total generalized variation regularized optical flow model","volume":"27","author":"Balle","year":"2019","journal-title":"Inverse Probl. Sci. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"116872","DOI":"10.1016\/j.jmatprotec.2020.116872","article-title":"Measurement of strain, strain rate and crack evolution in shear cutting","volume":"288","author":"Hartmann","year":"2021","journal-title":"J. Mater. Process. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.ymssp.2018.02.009","article-title":"Fault diagnosis of rotating machinery based on multiple probabilistic classifiers","volume":"108","author":"Zhong","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"108502","DOI":"10.1016\/j.measurement.2020.108502","article-title":"A Hybrid Deep-Learning Model for Fault Diagnosis of Rolling Bearings","volume":"169","author":"Xu","year":"2020","journal-title":"Measurement"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.isatra.2018.11.016","article-title":"Intelligent diagnosis method for machinery by sequential auto-reorganization of histogram","volume":"87","author":"Song","year":"2019","journal-title":"ISA Trans."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Piltan, F., and Kim, J.-M. (2018). Bearing Fault Diagnosis by a Robust Higher-Order Super-Twisting Sliding Mode Observer. Sensors, 18.","DOI":"10.3390\/s18041128"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zmarz\u0142y, P. (2020). Multi-Dimensional Mathematical Wear Models of Vibration Generated by Rolling Ball Bearings Made of AISI 52100 Bearing Steel. Materials, 13.","DOI":"10.3390\/ma13235440"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Piltan, F., and Kim, J.-M. (2019). Nonlinear Extended-state ARX-Laguerre PI Observer Fault Diagnosis of Bearings. Appl. Sci., 9.","DOI":"10.3390\/app9050888"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"116467","DOI":"10.1016\/j.energy.2019.116467","article-title":"State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression","volume":"190","author":"Li","year":"2020","journal-title":"Energy"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"106607","DOI":"10.1016\/j.ymssp.2019.106607","article-title":"Prediction of bearing failures by the analysis of the time series","volume":"139","author":"Soualhi","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Bai, Y.-T., Wang, X.-Y., Jin, X.-B., Zhao, Z.-Y., and Zhang, B.-H. (2020). A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model. Sensors, 20.","DOI":"10.3390\/s20010299"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"116159","DOI":"10.1016\/j.apenergy.2020.116159","article-title":"Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network","volume":"282","author":"Khaleghi","year":"2021","journal-title":"Appl. Energy"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Piltan, F., and Kim, J.-M. (2018). Bearing Fault Diagnosis Using an Extended Variable Structure Feedback Linearization Observer. Sensors, 18.","DOI":"10.3390\/s18124359"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"TayebiHaghighi, S., and Koo, I. (2021). SVM-Based Bearing Anomaly Identification with Self-Tuning Network-Fuzzy Robust Proportional Multi Integral and Smart Autoregressive Model. Appl. Sci., 11.","DOI":"10.3390\/app11062784"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"947","DOI":"10.3233\/JIFS-179461","article-title":"Advanced fuzzy-based leak detection and size estimation for pipelines","volume":"38","author":"Piltan","year":"2020","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"537","DOI":"10.2991\/ijcis.d.201228.002","article-title":"Fault Diagnosis of Bearings Using an Intelligence-Based Autoregressive Learning Lyapunov Algorithm","volume":"14","author":"Piltan","year":"2021","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mu, Y., Zhang, H., Xi, R., and Gao, Z. (2021). State and Fault Estimations for Discrete-Time T-S Fuzzy Systems with Sensor and Actuator Faults. IEEE Trans. Circuits Syst. II Express Briefs, 1.","DOI":"10.1109\/TCSII.2021.3067708"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3179","DOI":"10.1016\/j.camwa.2020.01.014","article-title":"Fractional sliding mode based on RBF neural network observer: Application to HIV infection mathematical model","volume":"79","author":"Sharafian","year":"2020","journal-title":"Comput. Math. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"101974","DOI":"10.1016\/j.rcim.2020.101974","article-title":"A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin","volume":"65","author":"Luo","year":"2020","journal-title":"Robot. Comput. Manuf."},{"key":"ref_24","first-page":"1","article-title":"Deep Learning-based Text Classification: A Comprehensive Review","volume":"54","author":"Shervin","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"107562","DOI":"10.1109\/ACCESS.2020.3001149","article-title":"Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"107574","DOI":"10.1016\/j.measurement.2020.107574","article-title":"Rolling bearing fault diagnosis using generalized refined composite multiscale sample entropy and optimized support vector machine","volume":"156","author":"Wang","year":"2020","journal-title":"Measurement"},{"key":"ref_27","unstructured":"Bearing Data Center (2020, December 23). Case Western Reserve University Seeded Fault Test Data. Available online: https:\/\/csegroups.case.edu\/bearingdatacenter\/pages\/welcome-case-western-reserve-university-bearing-data-center-website."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Piltan, F., and Kim, J.-M. (2021). Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning. Appl. Sci., 11.","DOI":"10.3390\/app11104602"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"10945","DOI":"10.1109\/TPEL.2020.2981500","article-title":"Online Anomaly Detection in DC\/DC Converters by Statistical Feature Esti-mation Using GPR and GA","volume":"35","author":"Yueming","year":"2020","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"107322","DOI":"10.1016\/j.asoc.2021.107322","article-title":"Ensemble of classification models with weighted functional link net-work","volume":"107","author":"Tanveer","year":"2021","journal-title":"Appl. Soft Comput."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/15\/5009\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T23:40:43Z","timestamp":1721086843000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/15\/5009"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,23]]},"references-count":30,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["s21155009"],"URL":"https:\/\/doi.org\/10.3390\/s21155009","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,23]]}}}