{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T15:33:48Z","timestamp":1791819228914,"version":"4.3.4"},"reference-count":30,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,12,31]],"date-time":"2019-12-31T00:00:00Z","timestamp":1577750400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Plan of China","award":["Grant No. 2016YFF0203305"],"award-info":[{"award-number":["Grant No. 2016YFF0203305"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities of China","doi-asserted-by":"publisher","award":["No. JD1912 and ZY1940"],"award-info":[{"award-number":["No. JD1912 and ZY1940"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Double First-rate Construction Special Funds","award":["No. ZD1601"],"award-info":[{"award-number":["No. ZD1601"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Diesel engine fault diagnosis is vital due to enhanced reliability and economic efficiency requirements. The extracted features in traditional fault diagnosis are constructed manually, which is very cumbersome because of the requirement for lots of expertise. To handle this issue, this paper proposed a variational stacked autoencoder (VSAE) to adaptively extract features from angular domain signals. As an unsupervised algorithm, VSAE can extract high-level features with the help of multiple encoding layers. Layer-wise pre-training and fine-tuning are introduced to get a better network initialization value. Moreover, the dropout technique and the batch normalization technique are carried out to prevent over-fitting and implement fast convergence. Finally, the harmony search optimizer (HSO) algorithm is introduced to get an appropriate hyper-parameter setting in the VSAE model, as well as make adaptive adjustment of the network structure. In order to verify the proposed method, the valve train fault data is collected on the diesel engine test rig under twelve operating conditions. The results indicate that the proposed scheme can effectively diagnose different degrees of intake valve fault, exhaust valve fault, and coupling fault under various operating conditions. Furthermore, the classification accuracy improved from 94.10% to 98.85%VSAE compared with stacked autoencoder (SAE) and some other traditional fault diagnosis algorithms.<\/jats:p>","DOI":"10.3390\/s20010223","type":"journal-article","created":{"date-parts":[[2020,1,3]],"date-time":"2020-01-03T03:28:53Z","timestamp":1578022133000},"page":"223","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["A Variational Stacked Autoencoder with Harmony Search Optimizer for Valve Train Fault Diagnosis of Diesel Engine"],"prefix":"10.3390","volume":"20","author":[{"given":"Kun","family":"Chen","sequence":"first","affiliation":[{"name":"Key Lab of Engine Health Monitoring-Control and Networking of Ministry of Education, Beijing University of Chemical Technology, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5839-5066","authenticated-orcid":false,"given":"Zhiwei","family":"Mao","sequence":"additional","affiliation":[{"name":"Key Lab of Engine Health Monitoring-Control and Networking of Ministry of Education, Beijing University of Chemical Technology, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haipeng","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Lab of Engine Health Monitoring-Control and Networking of Ministry of Education, Beijing University of Chemical Technology, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhinong","family":"Jiang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of High-End Mechanical Equipment Health Monitoring and Self-Recovery, Beijing University of Chemical Technology, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of High-End Mechanical Equipment Health Monitoring and Self-Recovery, Beijing University of Chemical Technology, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jiang, Z.N., Mao, Z.W., Wang, Z.J., and Zhang, J.J. (2017). Fault diagnosis of diesel engine valve clearance using the impact commencement detection method. Sensors, 17.","DOI":"10.3390\/s17122916"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1784\/insi.2018.60.8.418","article-title":"Vibration-based fault diagnosis method for conrod small-end bearing knock in diesel engines","volume":"60","author":"Mao","year":"2018","journal-title":"Insight"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhao, H.P., Zhang, J.J., Jiang, Z.N., Wei, D.H., Zhang, X.D., and Mao, Z.W. (2019). A new fault diagnosis method for a diesel engine based on an optimized vibration Mel frequency under multiple operating conditions. Sensors, 19.","DOI":"10.3390\/s19112590"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.engfailanal.2016.04.022","article-title":"Failure and thermo-mechanical stress analysis of the exhaust valve of diesel engine","volume":"66","author":"Witek","year":"2016","journal-title":"Eng. Fail. Anal."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1177\/0142331211408492","article-title":"Diesel engine valve clearance fault classification using multivariate analysis of variance and discriminant analysis","volume":"34","author":"Ftoutou","year":"2012","journal-title":"Trans. Inst. Meas. Control"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.ymssp.2015.10.024","article-title":"Fault detection and diagnosis of diesel engine valve trains","volume":"72","author":"Flett","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.ymssp.2009.06.012","article-title":"EMD-based fault diagnosis for abnormal clearance between contacting components in a diesel engine","volume":"24","author":"Li","year":"2010","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1016\/j.ymssp.2017.06.033","article-title":"Vibro-acoustic condition monitoring of internal combustion engines: A critical review of existing techniques","volume":"99","author":"Delvecchio","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1016\/j.ymssp.2015.10.037","article-title":"Extraction of fault component from abnormal sound in diesel engines using acoustic signals","volume":"75","author":"Ning","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_10","first-page":"625","article-title":"Extraction of fault component from abnormal sound in diesel engines using acoustic signals","volume":"136","author":"Wang","year":"2019","journal-title":"Measurement"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.apacoust.2018.09.002","article-title":"Fault detection of injectors in diesel engines using vibration time-frequency analysis","volume":"143","author":"Ahmad","year":"2019","journal-title":"Appl. Acoust."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.ymssp.2017.03.026","article-title":"Discriminative non-negative matrix factorization (DNMF) and its application to the fault diagnosis of diesel engine","volume":"95","author":"Yang","year":"2017","journal-title":"Appl. Mech. Syst. Signal Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.apacoust.2017.05.017","article-title":"The effect of piston scratching fault on the vibration behavior of an IC engine","volume":"126","author":"Moosavian","year":"2017","journal-title":"Appl. Acoust."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1016\/j.measurement.2018.08.038","article-title":"A novel intelligent detection method for rolling bearing based on IVMD and instantaneous energy distribution-permutation entropy","volume":"130","author":"Yan","year":"2018","journal-title":"Measurement"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_16","first-page":"1185","article-title":"Sparse feature learning for deep belief networks","volume":"20","author":"Ranzato","year":"2007","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2876","DOI":"10.3390\/s17122876","article-title":"A hybrid feature model and deep-learning-based bearing fault diagnosis","volume":"17","author":"Muhammad","year":"2017","journal-title":"Sensors"},{"key":"ref_18","first-page":"5105709","article-title":"A stacked autoencoder-based deep neural network for achieving gearbox fault diagnosis","volume":"2018","author":"Liu","year":"2018","journal-title":"Math. Probl. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3443","DOI":"10.3233\/JIFS-169524","article-title":"Fault diagnosis method of rotating machinery based on stacked denoising autoencoder","volume":"34","author":"Chen","year":"2018","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1016\/j.measurement.2018.08.010","article-title":"An enhancement denoising autoencoder for rolling bearing fault diagnosis","volume":"130","author":"Meng","year":"2018","journal-title":"Measurement"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.engappai.2019.04.013","article-title":"Process monitoring using variational autoencoder for high-dimensional nonlinear processes","volume":"83","author":"Lee","year":"2019","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"22554","DOI":"10.1109\/ACCESS.2019.2894764","article-title":"Systematic development of a new variational autoencoder model based on uncertain data for monitoring nonlinear processes","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.jprocont.2019.01.008","article-title":"Gaussian feature learning based on variational autoencoder for improving nonlinear process monitoring","volume":"75","author":"Zhang","year":"2019","journal-title":"J. Process. Control."},{"key":"ref_24","unstructured":"Kingma, D.P., and Welling, M. (2013). Auto-encoding variational bayes. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1177\/003754970107600201","article-title":"A new heuristic optimization algorithm: Harmony search","volume":"76","author":"Geem","year":"2001","journal-title":"Simulation"},{"key":"ref_26","first-page":"223","article-title":"Novel derivative of harmony search algorithm for discrete design variables","volume":"199","author":"Geem","year":"2008","journal-title":"Appl. Math. Comput."},{"key":"ref_27","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_29","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chen, K., Mao, Z.W., Zhao, H.P., and Zhang, J.J. (2019, January 15\u201317). Valve fault diagnosis of internal combustion engine based on an improved stacked autoencoder. Proceedings of the 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), Beijing, China.","DOI":"10.1109\/SDPC.2019.00060"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/1\/223\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:47:01Z","timestamp":1760190421000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/1\/223"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,31]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["s20010223"],"URL":"https:\/\/doi.org\/10.3390\/s20010223","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,31]]}}}