{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:16:26Z","timestamp":1760148986158,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,18]],"date-time":"2023-06-18T00:00:00Z","timestamp":1687046400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["51834006","52274158"],"award-info":[{"award-number":["51834006","52274158"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Rotating machinery is susceptible to harsh environmental interference, and fault signal features are challenging to extract, leading to difficulties in health status recognition. This paper proposes multi-scale hybrid features and improved convolutional neural networks (MSCCNN) health status identification methods for rotating machinery. Firstly, the rotating machinery vibration signal is decomposed into intrinsic modal components (IMF) using empirical wavelet decomposition, and multi-scale hybrid feature sets are constructed by simultaneously extracting time-domain, frequency-domain and time-frequency-domain features based on the original vibration signal and the intrinsic modal components it decomposes. Secondly, using correlation coefficients to select features sensitive to degradation, construct rotating machinery health indicators based on kernel principal component analysis and complete health state classification. Finally, a convolutional neural network model (MSCCNN) incorporating multi-scale convolution and hybrid attention mechanism modules is developed for health state identification of rotating machinery, and an improved custom loss function is applied to improve the superiority and generalization ability of the model. The bearing degradation data set of Xi\u2019an Jiaotong University is used to verify the effectiveness of the model. The recognition accuracy of the model is 98.22%, which is 5.83%, 3.30%, 2.29%, 1.52%, and 4.31% higher than that of SVM, CNN, CNN + CBAM, MSCNN, and MSCCNN + conventional features, respectively. The PHM2012 challenge dataset is used to increase the number of samples to validate the model effectiveness, and the model recognition accuracy is 97.67%, which is 5.63%, 1.88%, 1.36%, 1.49%, and 3.69% higher compared to SVM, CNN, CNN + CBAM, MSCNN, and MSCCNN + conventional features methods, respectively. The MSCCNN model recognition accuracy is 98.67% when validated on the degraded dataset of the reducer platform.<\/jats:p>","DOI":"10.3390\/s23125688","type":"journal-article","created":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T02:29:19Z","timestamp":1687141759000},"page":"5688","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Health Status Recognition Method for Rotating Machinery Based on Multi-Scale Hybrid Features and Improved Convolutional Neural Networks"],"prefix":"10.3390","volume":"23","author":[{"given":"Xiangang","family":"Cao","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"},{"name":"Shaanxi Province Key Laboratory of Intelligent Detection and Control of Mining Electromechanical Equipment, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingyu","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"},{"name":"Shaanxi Province Key Laboratory of Intelligent Detection and Control of Mining Electromechanical Equipment, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"},{"name":"Shaanxi Province Key Laboratory of Intelligent Detection and Control of Mining Electromechanical Equipment, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"},{"name":"Shaanxi Province Key Laboratory of Intelligent Detection and Control of Mining Electromechanical Equipment, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9891-0568","authenticated-orcid":false,"given":"Hongwei","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"},{"name":"Shaanxi Province Key Laboratory of Intelligent Detection and Control of Mining Electromechanical Equipment, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"},{"name":"Shaanxi Province Key Laboratory of Intelligent Detection and Control of Mining Electromechanical Equipment, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"161361","DOI":"10.1109\/ACCESS.2020.3021431","article-title":"Modified hierarchical multiscale dispersion entropy and its application to fault identification of rotating machinery","volume":"8","author":"Zhou","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/TIM.2019.2890933","article-title":"Novel adaptive search method for bearing fault frequency using stochastic resonance quantified by amplitude-domain index","volume":"69","author":"Huang","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.jsv.2014.09.025","article-title":"Vibration signal analysis using parameterized time\u2013frequency method for features extraction of varying-speed rotary machinery","volume":"335","author":"Yang","year":"2015","journal-title":"J. Sound Vib."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"107696","DOI":"10.1016\/j.ymssp.2021.107696","article-title":"Improved phase space warping method for degradation tracking of rotating machinery under variable working conditions","volume":"157","author":"Luo","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.ymssp.2013.01.017","article-title":"Recent advances in time\u2013frequency analysis methods for machinery fault diagnosis: A review with application examples","volume":"38","author":"Feng","year":"2013","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.asoc.2017.04.034","article-title":"Classification of ball bearing faults using a hybrid intelligent model","volume":"57","author":"Seera","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_7","first-page":"932","article-title":"Time-frequency extraction and state recognition of vibration signal of cylindrical roller","volume":"35","author":"Liu","year":"2022","journal-title":"J. Vib. Eng."},{"key":"ref_8","first-page":"210","article-title":"Domain adaptive fault diagnosis based on Transformer feature extraction for rotating machinery","volume":"43","author":"Huang","year":"2022","journal-title":"Chin. J. Sci. Instrum."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"105872","DOI":"10.1016\/j.engappai.2023.105872","article-title":"Multi-input CNN based vibro-acoustic fusion for accurate fault diagnosis of induction motor","volume":"120","author":"Choudhary","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_10","first-page":"1","article-title":"An intelligent fault diagnosis method based on domain adaptation and its application for bearings under polytropic working conditions","volume":"70","author":"Lei","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"doi-asserted-by":"crossref","unstructured":"Hsueh, Y.M., Ittangihal, V.R., Wu, W.B., Chang, H.C., and Kuo, C.C. (2019). Fault Diagnosis System for Induction Motors by CNN Using Empirical Wavelet Transform. Symmetry, 11.","key":"ref_11","DOI":"10.3390\/sym11101212"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1016\/j.promfg.2019.06.096","article-title":"Application of deep visualization in CNN-based tool condition monitoring for end milling","volume":"34","author":"Kothuru","year":"2019","journal-title":"Procedia Manuf."},{"key":"ref_13","first-page":"1","article-title":"Health Status Recognition of Gear Pump Based on Bayesian-LSTM","volume":"59","author":"Guo","year":"2023","journal-title":"J\/OL. J. Mech. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2930","DOI":"10.1049\/iet-gtd.2016.0848","article-title":"Reliability evaluation of WAMS using Markov-based graph theory approach","volume":"11","author":"Rana","year":"2017","journal-title":"Iet Gener. Transm. Distrib."},{"key":"ref_15","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_16","first-page":"234","article-title":"Health state recognition of harmonic reducer based on depth feature learning of voltage signal","volume":"42","author":"Chen","year":"2021","journal-title":"Chin. J. Sci. Instrum."},{"key":"ref_17","first-page":"206","article-title":"Application of deep learning in equipment prognostics and health management","volume":"40","author":"Chen","year":"2019","journal-title":"Chin. J. Sci. Instrum."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"49","DOI":"10.3901\/JME.2015.21.049","article-title":"Health monitoring method for big data of mechanical equipment based on deep learning theory","volume":"51","author":"Lei","year":"2015","journal-title":"J. Mech. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.measurement.2019.04.093","article-title":"A deep convolutional neural network based fusion method of two-direction vibration signal data for health state identification of planetary gearboxes","volume":"146","author":"Chen","year":"2019","journal-title":"Measurement"},{"key":"ref_20","first-page":"77","article-title":"Rolling bearing state recognition under variable condition using part-based representation of nonnegativity constrained autoencoder networks","volume":"41","author":"Zhang","year":"2020","journal-title":"Chin. J. Sci. Instrum."},{"key":"ref_21","first-page":"614","article-title":"Life status identification of rolling bearings under different working conditions by multi-source integrated GFK","volume":"33","author":"Chen","year":"2020","journal-title":"J. Vib. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"136","DOI":"10.3901\/JME.2019.23.136","article-title":"A Novel Fault Diagnosis Method Based on Improved Empirical Wavelet Transform and Maximum Correlated Kurtosis Deconvolution for Rolling Element Bearing","volume":"55","author":"Li","year":"2019","journal-title":"J. Mech. Eng."},{"key":"ref_23","first-page":"287","article-title":"Enhanced adaptive empirical Fourier decomposition based rolling bearing fault diagnosis method","volume":"41","author":"Cao","year":"2022","journal-title":"J. Vib. Shock"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1051\/jnwpu\/20224040771","article-title":"Study on single fractal characteristics of centrifugal compressor outlet dynamic pressure based on EWT","volume":"40","author":"Liu","year":"2022","journal-title":"J. Northwest. Polytech. Univ."},{"doi-asserted-by":"crossref","unstructured":"Lydia, A.A., and Francis, F.S. (2019, January 29\u201330). Convolutional neural network with an optimized backpropagation technique. Proceedings of the IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India.","key":"ref_25","DOI":"10.1109\/ICSCAN.2019.8878719"},{"doi-asserted-by":"crossref","unstructured":"Lau, M.M., and Lim, K.H. (2018, January 3\u20136). Review of adaptive activation function in deep neural network. Proceedings of the IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), Sarawak, Malaysia.","key":"ref_26","DOI":"10.1109\/IECBES.2018.8626714"},{"unstructured":"Zheng, Z. (2021). Research and implementation of feature selection method based on Gini correlation coefficient. [Master\u2019s Thesis, Zhengzhou University].","key":"ref_27"},{"key":"ref_28","first-page":"80","article-title":"Comparison between PCA and KPCA method in dimensional reduction of mechanical noise data","volume":"22","author":"Liang","year":"2011","journal-title":"China Mech. Eng."},{"key":"ref_29","first-page":"1","article-title":"XJTU-SY Rolling Element Bearing Accelerated Life Test Datasets: A Tutorial","volume":"55","author":"Lei","year":"2019","journal-title":"J. Mech. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.asoc.2018.06.038","article-title":"An integrated approach to bearing prognostics based on EEMD-multi feature extraction, Gaussian mixture models and Jensen-R\u00e9nyi divergence","volume":"71","author":"Rai","year":"2018","journal-title":"Appl. Soft Comput."},{"unstructured":"Blake, M.P., and Mitchel, W.S. (1972). Vibration and Acoustic Measurement, Spartan Books.","key":"ref_31"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"18332","DOI":"10.1109\/JSEN.2022.3197754","article-title":"Health Status Recognition of Rotating Machinery Based on Deep Residual Shrinkage Network under Time-varying Conditions","volume":"22","author":"Cao","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_33","first-page":"880","article-title":"Based on hybrid domain relative characteristics and FOA-XGBoost rolling bearing degradation evaluation","volume":"41","author":"Liu","year":"2021","journal-title":"Vib. Meas. Diagn."},{"unstructured":"Nectoux, P., Gouriveau, R., and Medjaher, K. (2012, January 18\u201321). Pronostia: An experimental platform for bearings accelerated degradation test. Proceedings of the IEEE International Conference on Prognostics and Health Management, Denver, CO, USA.","key":"ref_34"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/12\/5688\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:55:51Z","timestamp":1760126151000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/12\/5688"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,18]]},"references-count":34,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["s23125688"],"URL":"https:\/\/doi.org\/10.3390\/s23125688","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,6,18]]}}}