{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T22:10:12Z","timestamp":1766268612306,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,7]],"date-time":"2018-04-07T00:00:00Z","timestamp":1523059200000},"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":"<jats:p>The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE) signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN) architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC) method for combined modes. The NMEC-DNN is trained using data for a single fault and it classifies both single faults and multiple combined faults. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs). The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing fault diagnosis performance.<\/jats:p>","DOI":"10.3390\/s18041129","type":"journal-article","created":{"date-parts":[[2018,4,10]],"date-time":"2018-04-10T13:06:08Z","timestamp":1523365568000},"page":"1129","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9705-320X","authenticated-orcid":false,"given":"Bach Phi","family":"Duong","sequence":"first","affiliation":[{"name":"School of Electrical, Electronics and Computer Engineering, University of Ulsan, 44610 Ulsan, Korea"}]},{"given":"Jong-Myon","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Electrical, Electronics and Computer Engineering, University of Ulsan, 44610 Ulsan, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2683","DOI":"10.1109\/TIM.2010.2045927","article-title":"Detection of Motor Bearing Outer Raceway Defect by Wavelet Packet Transformed Motor Current Signature Analysis","volume":"59","author":"Lau","year":"2010","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.ymssp.2015.09.005","article-title":"Fault detection in reciprocating compressor valves under varying load conditions","volume":"70\u201371","author":"Pichler","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.automatica.2018.03.024","article-title":"Design of robust fuzzy fault detection filter for polynomial fuzzy systems with new finite frequency specifications","volume":"93","author":"Chibani","year":"2018","journal-title":"Automatica"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1109\/TFUZZ.2016.2641022","article-title":"Fault Detection Filtering for Nonhomogeneous Markovian Jump Systems via a Fuzzy Approach","volume":"26","author":"Li","year":"2018","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1109\/TFUZZ.2016.2593921","article-title":"Fuzzy Fault Detection Filter Design for T-S Fuzzy Systems in the Finite-Frequency Domain","volume":"25","author":"Chibani","year":"2017","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","unstructured":"Lughofer, E. (2018). Robust Data-Driven Fault Detection in Dynamic Process Environments Using Discrete Event Systems. Diagnosability, Security and Safety of Hybrid Dynamic and Cyber-Physical Systems, Springer.","DOI":"10.1007\/978-3-319-74962-4_4"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.asoc.2016.11.038","article-title":"Improved fault detection employing hybrid memetic fuzzy modeling and adaptive filters","volume":"51","author":"Serdio","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2248","DOI":"10.1016\/j.ymssp.2006.10.001","article-title":"A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology","volume":"21","author":"Dong","year":"2007","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.ymssp.2004.06.001","article-title":"Best basis-based intelligent machine fault diagnosis","volume":"19","author":"Zhang","year":"2005","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lee, H.-H., Nguyen, N.-T., and Kwon, J.-M. (2007). Bearing Diagnosis Using Time-Domain Features and Decision Tree. Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-540-74205-0_99"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.apacoust.2014.08.016","article-title":"Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals","volume":"89","author":"Fnaiech","year":"2015","journal-title":"Appl. Acoust."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3398","DOI":"10.1109\/TIE.2012.2219838","article-title":"Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks","volume":"60","author":"Prieto","year":"2013","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_14","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_15","first-page":"320508","article-title":"Multifault Diagnosis of Rolling Element Bearings Using a Wavelet Kurtogram and Vector Median-Based Feature Analysis","volume":"2015","author":"Nguyen","year":"2015","journal-title":"Shock Vib."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1109\/TE.2002.808234","article-title":"Basic vibration signal processing for bearing fault detection","volume":"46","author":"McInerny","year":"2003","journal-title":"IEEE Trans. Educ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1109\/TIE.2004.834971","article-title":"An amplitude modulation detector for fault diagnosis in rolling element bearings","volume":"51","author":"Stack","year":"2004","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"5623","DOI":"10.1109\/ACCESS.2017.2688467","article-title":"A Signal Theoretic Approach for Envelope Analysis of Real-Valued Signals","volume":"5","author":"Yang","year":"2017","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2763","DOI":"10.1109\/TPEL.2014.2356207","article-title":"High-Performance and Energy-Efficient Fault Diagnosis Using Effective Envelope Analysis and Denoising on a General-Purpose Graphics Processing Unit","volume":"30","author":"Kang","year":"2015","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Boser, B.E., Guyon, I.M., and Vapnik, V.N. (1992, January 27\u201329). A Training Algorithm for Optimal Margin Classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory, COLT \u201992, Pittsburgh, PA, USA.","DOI":"10.1145\/130385.130401"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Xue, H., Yang, Q., and Chen, S. (2009). SVM: Support Vector Machines. The Top Ten Algorithms in Data Mining, Chapman & Hall\/CRC.","DOI":"10.1201\/9781420089653.ch3"},{"key":"ref_24","unstructured":"Touretzky, D.S. (1989). An Application of the Principle of Maximum Information Preservation to Linear Systems. Advances in Neural Information Processing Systems 1 (NIPS\u201988), Morgan-Kaufmann."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1338","DOI":"10.1109\/TKDE.2006.162","article-title":"Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization","volume":"18","author":"Zhang","year":"2006","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_26","unstructured":"Dembczynski, K., Kotlowski, W., and Huellermeier, E. (July, January 27). Consistent Multilabel Ranking through Univariate Losses. In Proceedings of the 29th International Conference on Machine Learning, Edinburgh, Scotland."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kang, M., Ramaswami, G.K., Hodkiewicz, M., Cripps, E., Kim, J.-M., and Pecht, M. (2016). A Sequential k-Nearest Neighbor Classification Approach for Data-Driven Fault Diagnosis Using Distance- and Density-Based Affinity Measures. Data Mining and Big Data, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-319-40973-3_25"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tra, V., Kim, J., Khan, S.A., and Kim, J.-M. (2017). Bearing Fault Diagnosis under Variable Speed Using Convolutional Neural Networks and the Stochastic Diagonal Levenberg-Marquardt Algorithm. Sensors, 17.","DOI":"10.3390\/s17122834"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2363","DOI":"10.1109\/TIE.2011.2167893","article-title":"Local and Nonlocal Preserving Projection for Bearing Defect Classification and Performance Assessment","volume":"59","author":"Yu","year":"2012","journal-title":"IEEE Trans. Ind. Electron."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/4\/1129\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:59:55Z","timestamp":1760194795000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/4\/1129"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,7]]},"references-count":29,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2018,4]]}},"alternative-id":["s18041129"],"URL":"https:\/\/doi.org\/10.3390\/s18041129","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2018,4,7]]}}}