{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T09:12:23Z","timestamp":1774084343683,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T00:00:00Z","timestamp":1668643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52165012"],"award-info":[{"award-number":["52165012"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["20212ACB202004"],"award-info":[{"award-number":["20212ACB202004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Jiangxi Province in China","award":["52165012"],"award-info":[{"award-number":["52165012"]}]},{"name":"Natural Science Foundation of Jiangxi Province in China","award":["20212ACB202004"],"award-info":[{"award-number":["20212ACB202004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the rapid development of fault prognostics and health management (PHM) technology, more and more deep learning algorithms have been applied to the intelligent fault diagnosis of rolling bearings, and although all of them can achieve over 90% diagnostic accuracy, the generality and robustness of the models cannot be truly verified under complex extreme variable loading conditions. In this study, an end-to-end rolling bearing fault diagnosis model of a hybrid deep neural network with principal component analysis is proposed. Firstly, in order to reduce the complexity of deep learning computation, data pre-processing is performed by principal component analysis (PCA) with feature dimensionality reduction. The preprocessed data is imported into the hybrid deep learning model. The first layer of the model uses a CNN algorithm for denoising and simple feature extraction, the second layer makes use of bi-directional long and short memory (BiLSTM) for greater in-depth extraction of the data with time series features, and the last layer uses an attention mechanism for optimal weight assignment, which can further improve the diagnostic precision. The test accuracy of this model is fully comparable to existing deep learning fault diagnosis models, especially under low load; the test accuracy is 100% at constant load and nearly 90% for variable load, and the test accuracy is 72.8% at extreme variable load (2.205 N\u00b7m\/s\u20130.735 N\u00b7m\/s and 0.735 N\u00b7m\/s\u20132.205 N\u00b7m\/s), which are the worst possible load conditions. The experimental results fully prove that the model has reliable robustness and generality.<\/jats:p>","DOI":"10.3390\/s22228906","type":"journal-article","created":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T06:11:34Z","timestamp":1668751894000},"page":"8906","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":73,"title":["Rolling Bearing Fault Diagnosis Using Hybrid Neural Network with Principal Component Analysis"],"prefix":"10.3390","volume":"22","author":[{"given":"Keshun","family":"You","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China"}]},{"given":"Guangqi","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8125-945X","authenticated-orcid":false,"given":"Yingkui","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,17]]},"reference":[{"key":"ref_1","unstructured":"Lanham, C. (2002). Understanding the Tests That Are Recommended for Electric Motor Predictive Maintenance, Baker Instrument Company. Energy Publication."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.neucom.2018.06.078","article-title":"A survey on Deep Learning based bearing fault diagnosis","volume":"335","author":"Hoang","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/j.isatra.2022.04.026","article-title":"Intelligent fault diagnosis of rolling bearings under varying operating conditions based on domain-adversarial neural network and attention mechanism","volume":"130","author":"Wu","year":"2022","journal-title":"ISA Trans."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"14347","DOI":"10.1109\/ACCESS.2017.2720965","article-title":"Transfer Learning with Neural Networks for Bearing Fault Diagnosis in Changing Working Conditions","volume":"5","author":"Zhang","year":"2017","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"78502","DOI":"10.1109\/ACCESS.2022.3192441","article-title":"A Cross Working Condition Multiscale Recursive Feature Fusion Method for Fault Diagnosis of Rolling Bearing in Multiple Working Conditions","volume":"10","author":"Zhang","year":"2022","journal-title":"IEEE Access"},{"key":"ref_6","unstructured":"(2004). Rolling Bearings: Damage and Failures\u2014Terms, Characteristics and Causes (Standard No. ISO 15243)."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.ymssp.2018.02.016","article-title":"Artificial intelligence for fault diagnosis of rotating machinery: A review","volume":"108","author":"Liu","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"513","DOI":"10.2507\/IJSIMM20-3-568","article-title":"Effect of Radial Clearance on Ball Bearing\u2019s Dynamics Using a 2-DOF Model","volume":"20","author":"Ambrozkiewicz","year":"2021","journal-title":"Int. J. Simul. Model."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"109545","DOI":"10.1016\/j.ymssp.2022.109545","article-title":"Vibration characteristics of rotor-bearing system with angular misalignment and cage fracture: Simulation and experiment","volume":"182","author":"Wang","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.ymssp.2017.11.024","article-title":"A review on the application of deep learning in system health management","volume":"107","author":"Khan","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.ymssp.2017.06.012","article-title":"A review on data-driven fault severity assessment in rolling bearings","volume":"99","author":"Cerrada","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.ymssp.2018.05.050","article-title":"Deep learning and its applications to machine health monitoring","volume":"115","author":"Zhao","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2022.3185323","article-title":"A Multisensor Information Fusion Method for High-Reliability Fault Diagnosis of Rotating Machinery","volume":"71","author":"Huo","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, S., Zhang, S., Wang, B., and Habetler, T.G. (2019, January 27\u201330). Deep Learning Algorithms for Bearing Fault Diagnostics\u2014A Comprehensive Review. Proceedings of the 2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), Toulouse, France.","DOI":"10.1109\/DEMPED.2019.8864915"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Soualhi, A., and Taleb, S. (2018, January 20\u201322). Data Fusion for Fault Severity Estimation of Ball Bearings. Proceedings of the 2018 IEEE International Conference on Industrial Technology (ICIT), Lyon, France.","DOI":"10.1109\/ICIT.2018.8352514"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1109\/TIE.2018.2811366","article-title":"Degradation Data-Driven Time-To-Failure Prognostics Approach for Rolling Element Bearings in Electrical Machines","volume":"66","author":"Wu","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"8472","DOI":"10.1109\/JSEN.2018.2866708","article-title":"Fault Diagnosis Based on Weighted Extreme Learning Machine With Wavelet Packet Decomposition and KPCA","volume":"18","author":"Hu","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kang, S., Cui, L., Wang, Y., Li, F., and Mikulovich, V.I. (2017, January 9\u201312). Method of assessing the multi-state of a rolling bearing based on CFOA-HSVM two measures combination. Proceedings of the 2017 Prognostics and System Health Management Conference (PHM-Harbin), Harbin, China.","DOI":"10.1109\/PHM.2017.8079180"},{"key":"ref_19","first-page":"316","article-title":"Data-Driven Fault Classification Using Support Vector Machines","volume":"1322","author":"Jallepalli","year":"2021","journal-title":"Intell. Hum. Syst. Integr."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5618","DOI":"10.1109\/JSEN.2017.2727638","article-title":"Analysis of Statistical Time-Domain Features Effectiveness in Identification of Bearing Faults From Vibration Signal","volume":"17","author":"Nayana","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1109\/TEC.2003.811739","article-title":"Application of AI tools in fault diagnosis of electrical machines and drives-an overview","volume":"18","author":"Awadallah","year":"2003","journal-title":"IEEE Trans. Energy Convers."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1109\/TEC.2005.847955","article-title":"Condition monitoring and fault diagnosis of electrical motors\u2014A review","volume":"20","author":"Nandi","year":"2005","journal-title":"IEEE Trans. Energy Convers."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1109\/41.107100","article-title":"A neural network approach to real-time condition monitoring of induction motors","volume":"38","author":"Chow","year":"1991","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1517","DOI":"10.1109\/TIM.2004.834070","article-title":"PCA-Based Feature Selection Scheme for Machine Defect Classification","volume":"53","author":"Malhi","year":"2004","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.isatra.2016.10.014","article-title":"A hybrid fault diagnosis approach based on mixed-domain state features for rotating machinery","volume":"66","author":"Xue","year":"2017","journal-title":"ISA Trans."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ettefagh, M.M., Ghaemi, M., and Asr, M.Y. (2014, January 23\u201325). Bearing fault diagnosis using hybrid genetic algorithm K-means clustering. Proceedings of the 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings, Alberobello, Italy.","DOI":"10.1109\/INISTA.2014.6873601"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Teotrakool, K., Devaney, M.J., and Eren, L. (2008, January 12\u201315). Bearing Fault Detection in Adjustable Speed Drives via a Support Vector Machine with Feature Selection using a Genetic Algorithm. Proceedings of the 2008 IEEE Instrumentation and Measurement Technology Conference, Victoria, BC, Canada.","DOI":"10.1109\/IMTC.2008.4547208"},{"key":"ref_28","first-page":"7135","article-title":"Optimization of dewatering process of concentrate pressure filtering by support vector regression","volume":"12","author":"Liu","year":"2022","journal-title":"Sci. Reports"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1109\/TIM.2011.2169182","article-title":"Prediction of Machine Health Condition Using Neuro-Fuzzy and Bayesian Algorithms","volume":"61","author":"Chen","year":"2011","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1109\/TIM.2013.2245180","article-title":"Semisupervised distance-preserving self-organizing map for machine-defect detection and classification","volume":"62","author":"Li","year":"2013","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5515","DOI":"10.1109\/ACCESS.2017.2675940","article-title":"A Fault Diagnosis Approach for Rolling Element Bearings Based on RSGWPT-LCD Bilayer Screening and Extreme Learning Machine","volume":"5","author":"Tong","year":"2017","journal-title":"IEEE Access"},{"key":"ref_32","unstructured":"Shen, F., Chen, C., Yan, R., and Gao, R.X. (2015, January 21\u201323). Bearing fault diagnosis based on SVD feature extraction and transfer learning classification. Proceedings of the 2015 Prognostics and System Health Management Conference (PHM), Beijing, China."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.neucom.2018.07.038","article-title":"Fault diagnosis of rolling bearing based on feature reduction with global-local margin Fisher analysis","volume":"315","author":"Zhao","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2256","DOI":"10.1109\/TII.2012.2231084","article-title":"Robust Diagnosis of Rolling Element Bearings Based on Classification Techniques","volume":"9","author":"Cococcioni","year":"2012","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5581","DOI":"10.1109\/JSEN.2017.2726011","article-title":"Fault Diagnosis of a Rolling Bearing Using Wavelet Packet Denoising and Random Forests","volume":"17","author":"Wang","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1109\/TIM.2015.2498978","article-title":"Feature Knowledge Based Fault Detection of Induction Motors Through the Analysis of Stator Current Data","volume":"65","author":"Yang","year":"2016","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIE.2012.2192894","article-title":"Plastic Bearing Fault Diagnosis Based on a Two-Step Data Mining Approach","volume":"60","author":"He","year":"2012","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"12045","DOI":"10.1088\/1742-6596\/842\/1\/012045","article-title":"Topic Correlation Analysis for Bearing Fault Diagnosis Under Variable Operating Conditions","volume":"842","author":"Chen","year":"2017","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.knosys.2016.10.022","article-title":"A novel intelligent method for bearing fault diagnosis based on affinity propagation clustering and adaptive feature selection","volume":"116","author":"Wei","year":"2017","journal-title":"Knowl. Based Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/s00170-018-2902-0","article-title":"Adaptive online dictionary learning for bearing fault diagnosis","volume":"101","author":"Lu","year":"2018","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.jsv.2016.05.027","article-title":"Convolutional Neural Network Based Fault Detection for Rotating Machinery","volume":"377","author":"Janssens","year":"2016","journal-title":"J. Sound Vib."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.measurement.2016.07.054","article-title":"Hierarchical adaptive deep convolutionneural network and its application to bearing fault diagnosis","volume":"93","author":"Guo","year":"2016","journal-title":"Measurement"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5990","DOI":"10.1109\/TIE.2017.2774777","article-title":"A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method","volume":"65","author":"Wen","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/MCSE.2018.110113254","article-title":"Fault State Recognition of Rolling Bearing Based Fully Convolutional Network","volume":"21","author":"Zhang","year":"2019","journal-title":"Comput. Sci. Eng."},{"key":"ref_45","unstructured":"Zhuang, Z., and Wei, Q. (2018, January 27\u201329). Intelligent fault diagnosis of rolling bearing using one-dimensional multi-scale deep convolutional neural network based health state classification. Proceedings of the 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC), Zhuhai, China."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Guo, S., Yang, T., Gao, W., Zhang, C., and Zhang, Y. (2018). An Intelligent Fault Diagnosis Method for Bearings with Variable Rotating Speed Based on Pythagorean Spatial Pyramid Pooling CNN. Sensors, 18.","DOI":"10.3390\/s18113857"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"095009","DOI":"10.1088\/1361-6501\/aad101","article-title":"An intelligent fault diagnosis framework for raw vibration signals: Adaptive overlapping convolutional neural network","volume":"29","author":"Qian","year":"2018","journal-title":"Meas. Sci. Technol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.ymssp.2017.03.034","article-title":"A novel deep autoencoder feature learning method for rotating machinery fault diagnosis","volume":"95","author":"Shao","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1109\/TIM.2017.2669947","article-title":"Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network","volume":"66","author":"Chen","year":"2017","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/s40313-015-0173-7","article-title":"A Robust Bearing Fault Detection and Diagnosis Technique for Brushless DC Motors Under Non-stationary Operating Conditions","volume":"26","author":"Abed","year":"2015","journal-title":"J. Control. Autom. Electr. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Xie, Y., and Zhang, T. (2018, January 25\u201327). Imbalanced Learning for Fault Diagnosis Problem of Rotating Machinery Based on Generative Adversarial Networks. Proceedings of the 2018 37th Chinese Control Conference (CCC), Wuhan, China.","DOI":"10.23919\/ChiCC.2018.8483334"},{"key":"ref_52","first-page":"443","article-title":"An improved bearing fault diagnosis method using one-dimensional CNN and LSTM","volume":"64","author":"Pan","year":"2018","journal-title":"J. Mech. Eng."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.ymssp.2017.06.022","article-title":"A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load","volume":"100","author":"Zhang","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"108653","DOI":"10.1016\/j.ymssp.2021.108653","article-title":"The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study","volume":"168","author":"Li","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"106090","DOI":"10.1016\/j.resconrec.2021.106090","article-title":"Garbage classification system based on improved shufflenet v2","volume":"178","author":"Chen","year":"2022","journal-title":"Resour. Conserv. Recycling"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"111594","DOI":"10.1016\/j.measurement.2022.111594","article-title":"Attention mechanism in intelligent fault diagnosis of machinery: A review of technique and application","volume":"199","author":"Lv","year":"2022","journal-title":"Measurement"},{"key":"ref_57","unstructured":"(2022, September 11). Case Western Reserve University (CWRU) Bearing Data Center, December 2018. Available online: https:\/\/engineering.case.edu\/bearingdatacenter."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.ymssp.2015.04.021","article-title":"Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study","volume":"64","author":"Smith","year":"2015","journal-title":"Mech. Syst. Signal Process."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/8906\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:20:40Z","timestamp":1760145640000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/8906"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,17]]},"references-count":58,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22228906"],"URL":"https:\/\/doi.org\/10.3390\/s22228906","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,17]]}}}