{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T14:49:36Z","timestamp":1778338176865,"version":"3.51.4"},"reference-count":47,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2017,12,11]],"date-time":"2017-12-11T00:00:00Z","timestamp":1512950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007053","name":"Korea Institute of Energy Technology Evaluation and Planning","doi-asserted-by":"publisher","award":["20162220100050"],"award-info":[{"award-number":["20162220100050"]}],"id":[{"id":"10.13039\/501100007053","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007053","name":"Korea Institute of Energy Technology Evaluation and Planning","doi-asserted-by":"publisher","award":["20161120100350"],"award-info":[{"award-number":["20161120100350"]}],"id":[{"id":"10.13039\/501100007053","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007053","name":"Korea Institute of Energy Technology Evaluation and Planning","doi-asserted-by":"publisher","award":[". 20172510102130"],"award-info":[{"award-number":[". 20172510102130"]}],"id":[{"id":"10.13039\/501100007053","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2016H1D5A1910564"],"award-info":[{"award-number":["NRF-2016H1D5A1910564"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2016R1D1A3B03931927"],"award-info":[{"award-number":["2016R1D1A3B03931927"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE)-based deep neural networks (DNNs) to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs) and backpropagation neural networks (BPNNs).<\/jats:p>","DOI":"10.3390\/s17122876","type":"journal-article","created":{"date-parts":[[2017,12,11]],"date-time":"2017-12-11T12:26:37Z","timestamp":1512995197000},"page":"2876","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":184,"title":["A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis"],"prefix":"10.3390","volume":"17","author":[{"given":"Muhammad","family":"Sohaib","sequence":"first","affiliation":[{"name":"Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Korea"}]},{"given":"Cheol-Hong","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Electronics and Computer Engineering, Chonnam National University, Gwangju 61186, Korea"}]},{"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":[[2017,12,11]]},"reference":[{"key":"ref_1","unstructured":"Toliyat, H.A., Nandi, S., Choi, S., and Meshgin-Kelk, H. (2012). Electric Machines: Modeling, Condition Monitoring and Fault Diagnosis, CRC Press."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1846","DOI":"10.1109\/TIE.2014.2361115","article-title":"Induction machine bearing fault detection by means of statistical processing of the stray flux measurement","volume":"62","author":"Frosini","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"EL89","DOI":"10.1121\/1.4976038","article-title":"Reliable bearing fault diagnosis using Bayesian inference-based multi-class support vector machines","volume":"141","author":"Islam","year":"2017","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Khan, S.A., and Kim, J.-M. (2016). Automated Bearing Fault Diagnosis Using 2D Analysis of Vibration Acceleration Signals under Variable Speed Conditions. Shock Vib., 2016.","DOI":"10.1155\/2016\/8729572"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Jiang, X., Wu, L., and Ge, M. (2017). A Novel Faults Diagnosis Method for Rolling Element Bearings Based on EWT and Ambiguity Correlation Classifiers. Entropy, 19.","DOI":"10.3390\/e19050231"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, S., Liu, G., Tang, X., Lu, J., and Hu, J. (2017). An Ensemble Deep Convolutional Neural Network Model with Improved DS Evidence Fusion for Bearing Fault Diagnosis. Sensors, 17.","DOI":"10.3390\/s17081729"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, R., Peng, Z., Wu, L., Yao, B., and Guan, Y. (2017). Fault Diagnosis from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence. Sensors, 17.","DOI":"10.3390\/s17030549"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2524","DOI":"10.1016\/j.jfranklin.2016.09.020","article-title":"Actuator and sensor faults estimation based on proportional integral observer for TS fuzzy model","volume":"354","author":"Youssef","year":"2017","journal-title":"J. Frankl. Inst."},{"key":"ref_9","first-page":"1","article-title":"Diagnostic Observer Design for T-S Fuzzy Systems: Application to Real-Time Weighted Fault Detection Approach","volume":"PP","author":"Li","year":"2017","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1109\/TFUZZ.2016.2593921","article-title":"Fuzzy Fault Detection Filter Design for T\u2013S Fuzzy Systems in the Finite-Frequency Domain","volume":"25","author":"Chibani","year":"2017","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_11","first-page":"1","article-title":"Fault Diagnosis of Power Systems Using Intuitionistic Fuzzy Spiking Neural P Systems","volume":"PP","author":"Peng","year":"2017","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1016\/j.ymssp.2005.09.012","article-title":"A review on machinery diagnostics and prognostics implementing condition-based maintenance","volume":"20","author":"Jardine","year":"2006","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/MIM.2013.6495676","article-title":"Ball bearing damage detection using traditional signal processing algorithms","volume":"16","author":"Bediaga","year":"2013","journal-title":"IEEE Instrum. Meas. Mag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"EL35","DOI":"10.1121\/1.4991329","article-title":"Incipient fault diagnosis in bearings under variable speed conditions using multiresolution analysis and a weighted committee machine","volume":"142","author":"Tra","year":"2017","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1109\/TIE.2014.2327589","article-title":"Heterogeneous feature models and feature selection applied to bearing fault diagnosis","volume":"62","author":"Rauber","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2501","DOI":"10.1109\/TIE.2016.2522944","article-title":"Diagnosis and prognosis for complicated industrial systems\u2014Part I","volume":"63","author":"Yin","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3201","DOI":"10.1109\/TIE.2016.2538745","article-title":"Diagnosis and prognosis for complicated industrial systems\u2014Part II","volume":"63","author":"Yin","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_18","unstructured":"Chang, T.C., Wysk, R., and Wang, H. (1991). Computer-Aided Manufacturing, Prentice Hall."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Qin, X., Li, Q., Dong, X., and Lv, S. (2017). The Fault Diagnosis of Rolling Bearing Based on Ensemble Empirical Mode Decomposition and Random Forest. Shock Vib., 2017.","DOI":"10.1155\/2017\/2623081"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.ymssp.2013.04.006","article-title":"Quantitative diagnosis of a spall-like fault of a rolling element bearing by empirical mode decomposition and the approximate entropy method","volume":"40","author":"Zhao","year":"2013","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Huang, W., Sun, H., and Wang, W. (2017). Resonance-Based Sparse Signal Decomposition and its Application in Mechanical Fault Diagnosis: A Review. Sensors, 17.","DOI":"10.3390\/s17061279"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.ymssp.2016.06.033","article-title":"Envelope analysis of rotating machine vibrations in variable speed conditions: A comprehensive treatment","volume":"84","author":"Abboud","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhou, S., Tang, B., and Chen, R. (2009, January 8\u20139). Comparison between non-stationary signals fast Fourier transform and wavelet analysis. Proceedings of the International Asia Symposium on Intelligent Interaction and Affective Computing, Wuhan, China.","DOI":"10.1109\/ASIA.2009.31"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1555","DOI":"10.1109\/TSP.2012.2236834","article-title":"Fractional Fourier Analysis of Random Signals and the Notion of\/spl alpha\/-Stationarity of the Wigner-Ville Distribution","volume":"61","author":"Torres","year":"2013","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1109\/TIM.2016.2647458","article-title":"Short-Frequency Fourier Transform for Fault Diagnosis of Induction Machines Working in Transient Regime","volume":"66","year":"2017","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, X., Jiang, D., Han, T., Wang, N., Yang, W., and Yang, Y. (2017). Rotating Machinery Fault Diagnosis for Imbalanced Data Based on Fast Clustering Algorithm and Support Vector Machine. J. Sens., 2017.","DOI":"10.1155\/2017\/8092691"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Islam, M.M., and Kim, J.-M. (2017). Time-frequency envelope analysis-based sub-band selection and probabilistic support vector machines for multi-fault diagnosis of low-speed bearings. J. Ambient Intell. Humaniz. Comput., 1\u201316.","DOI":"10.1007\/s12652-017-0585-2"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ymssp.2015.08.023","article-title":"Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review","volume":"70","author":"Chen","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"He, S., Liu, Y., Chen, J., and Zi, Y. (2017). Wavelet Transform Based on Inner Product for Fault Diagnosis of Rotating Machinery. Structural Health Monitoring, Springer.","DOI":"10.1007\/978-3-319-56126-4_4"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1800","DOI":"10.1109\/ACCESS.2016.2555902","article-title":"An experimental study of clogging fault diagnosis in heat exchangers based on vibration signals","volume":"4","author":"Huang","year":"2016","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3299","DOI":"10.1109\/TIE.2016.2527623","article-title":"A hybrid feature selection scheme for reducing diagnostic performance deterioration caused by outliers in data-driven diagnostics","volume":"63","author":"Kang","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1049\/iet-epa:20070280","article-title":"Review of condition monitoring of rotating electrical machines","volume":"2","author":"Tavner","year":"2008","journal-title":"IET Electr. Power Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/j.ymssp.2015.04.039","article-title":"Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications","volume":"66","author":"Wang","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Dong, S., Chen, L., Tang, B., Xu, X., Gao, Z., and Liu, J. (2015). Rotating machine fault diagnosis based on optimal morphological filter and local tangent space alignment. Shock Vib., 2015.","DOI":"10.1155\/2015\/893504"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Verstraete, D., Ferrada, A., Droguett, E.L., Meruane, V., and Modarres, M. (2017). Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings. Shock Vib., 2017.","DOI":"10.1155\/2017\/5067651"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yang, D., Mu, H., Xu, Z., Wang, Z., Yi, C., and Liu, C. (2017). Based on Soft Competition ART Neural Network Ensemble and Its Application to the Fault Diagnosis of Bearing. Math. Probl. Eng., 2017.","DOI":"10.1155\/2017\/2641546"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"19442","DOI":"10.1109\/ACCESS.2017.2661967","article-title":"Incipient fault diagnosis of roller bearing using optimized wavelet transform based multi-speed vibration signatures","volume":"5","author":"Huo","year":"2017","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/ACCESS.2016.2608505","article-title":"Enhancing Fault Classification Accuracy of Ball Bearing Using Central Tendency Based Time Domain Features","volume":"5","author":"Tahir","year":"2016","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"6027","DOI":"10.1109\/ACCESS.2017.2693379","article-title":"Rolling Element Bearing Fault Diagnosis Using Improved Manifold Learning","volume":"5","author":"Yao","year":"2017","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"7749","DOI":"10.1109\/TIE.2015.2460242","article-title":"Time-varying and multiresolution envelope analysis and discriminative feature analysis for bearing fault diagnosis","volume":"62","author":"Kang","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"13694","DOI":"10.3390\/s121013694","article-title":"Spectral regression based fault feature extraction for bearing accelerometer sensor signals","volume":"12","author":"Xia","year":"2012","journal-title":"Sensors"},{"key":"ref_42","first-page":"3429","article-title":"Plastic bearing fault diagnosis based on a two-step data mining approach","volume":"60","author":"He","year":"2013","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1016","DOI":"10.1049\/iet-smt.2015.0026","article-title":"Bearing fault classification using ANN-based Hilbert footprint analysis","volume":"9","author":"Dubey","year":"2015","journal-title":"IET Sci. Meas. Technol."},{"key":"ref_44","unstructured":"Nikolaou, N.G., and Antoniadis, I.A. (2001, January 8\u201315). Application of Wavelet Packets in Bearing Fault Diagnosis. Proceedings of the 5th WSES International Conference on Circuits, Systems, Communications and Computers (CSCC 2001), Rethymno, Greece."},{"key":"ref_45","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_46","unstructured":"Case Western Reserve University (2017, January 21). B.D.C. Seeded Fault Test Data. Available online: http:\/\/csegroups.case.edu\/bearingdatacenter\/home\/."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1109\/TIE.2014.2327555","article-title":"Vibration spectrum imaging: A novel bearing fault classification approach","volume":"62","author":"Amar","year":"2015","journal-title":"IEEE Trans. Ind. Electron."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/12\/2876\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:53:30Z","timestamp":1760208810000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/12\/2876"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,12,11]]},"references-count":47,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2017,12]]}},"alternative-id":["s17122876"],"URL":"https:\/\/doi.org\/10.3390\/s17122876","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,12,11]]}}}