{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T21:34:07Z","timestamp":1763156047054,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2019,7,25]],"date-time":"2019-07-25T00:00:00Z","timestamp":1564012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Technology R&amp;D Program of China","award":["2017YFD0700602 and 2018YFB1702503"],"award-info":[{"award-number":["2017YFD0700602 and 2018YFB1702503"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate and timely misfire fault diagnosis is of vital significance for diesel engines. However, existing algorithms are prone to fall into model over-fitting and adopt low energy-concentrated features. This paper presents a novel extreme gradient boosting-based misfire fault diagnosis approach utilizing the high-accuracy time\u2013frequency information of vibration signals. First, diesel engine misfire tests were conducted under different spindle speeds, and the corresponding vibration signals were acquired via a triaxial accelerometer. The time-domain features of signals were extracted by using a time-domain statistics method, while the high-accuracy time\u2013frequency domain features were obtained via the high-resolution multisynchrosqueezing transform. Thereafter, considering the nonlinearity and high dimensionality of the original characteristic data sets, the locally linear embedding method was employed for feature dimensionality reduction. Eventually, to avoid model overfitting, the extreme gradient boosting algorithm was utilized for diesel engine misfire fault diagnosis. Experiments under different spindle speeds and comprehensive comparisons with other evaluation methods were conducted to demonstrate the effectiveness of the proposed extreme gradient boosting-based misfire diagnosis method. The results verify that the highest classification accuracy of the proposed extreme gradient boosting-based algorithm is up to 99.93%. Simultaneously, the classification accuracy of the presented approach is approximately 24.63% higher on average than those of algorithms that use wavelet packet-based features. Moreover, it is shown that it obtains the minimum root mean squared error and can effectively prevent the model from falling into overfitting.<\/jats:p>","DOI":"10.3390\/s19153280","type":"journal-article","created":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T08:45:39Z","timestamp":1564130739000},"page":"3280","update-policy":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Intelligent Fault Diagnosis of Diesel Engines via Extreme Gradient Boosting and High-Accuracy Time\u2013Frequency Information of Vibration Signals"],"prefix":"10.3390","volume":"19","author":[{"given":"Jianfeng","family":"Tao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}]},{"ORCID":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/orcid.org\/0000-0002-5200-3241","authenticated-orcid":false,"given":"Chengjin","family":"Qin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}]},{"given":"Weixing","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}]},{"given":"Chengliang","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,25]]},"reference":[{"key":"ref_1","first-page":"86","article-title":"Diesel Engine Fault Detection Using Vibration and Acoustic Emission Signals","volume":"4","author":"Allam","year":"2018","journal-title":"Int. J. Adv. Sci. Res. Eng."},{"key":"ref_2","first-page":"25","article-title":"Misfire detection in a spark ignition engine using support vector machines","volume":"5","author":"Devasenapati","year":"2010","journal-title":"Int. J. Comput. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2150","DOI":"10.1016\/j.eswa.2009.07.061","article-title":"Misfire identification in a four-stroke four-cylinder petrol engine using decision tree","volume":"37","author":"Devasenapati","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1177\/0954406217691554","article-title":"Diesel engine fault diagnosis using intrinsic time-scale decomposition and multistage Adaboost relevance vector machine","volume":"332","author":"Liu","year":"2018","journal-title":"Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3757","DOI":"10.1109\/TIE.2015.2417501","article-title":"A survey of fault diagnosis and fault-tolerant techniques\u2014Part I: Fault diagnosis with model-based and signal-based approaches","volume":"62","author":"Gao","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3768","DOI":"10.1109\/TIE.2015.2417501","article-title":"A survey of fault diagnosis and fault-tolerant techniques\u2014Part II: Fault diagnosis with knowledge-based and hybrid\/active-based approaches","volume":"62","author":"Gao","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.renene.2017.05.020","article-title":"Hybrid method for remaining useful life prediction in wind turbine systems","volume":"116","author":"Djeziri","year":"2018","journal-title":"Renew. Energy"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.jprocont.2016.10.003","article-title":"Fault prognosis for batch production based on percentile measure and gamma process: Application to semiconductor manufacturing","volume":"48","author":"Nguyen","year":"2016","journal-title":"J. Process Control"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.1049\/iet-smt.2017.0005","article-title":"Remaining useful life estimation without needing for prior knowledge of the degradation features","volume":"11","author":"Benmoussa","year":"2017","journal-title":"IET Sci. Meas. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.arcontrol.2004.12.002","article-title":"Diesel engine fault diagnosis using intrinsic time-scale decomposition and multistage Adaboost relevance vector machine","volume":"29","author":"Isermann","year":"2005","journal-title":"Annu. Rev. Control"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.ymssp.2013.06.004","article-title":"Prognostics and health management design for rotary machinery systems\u2014Reviews, methodology and applications","volume":"42","author":"Lee","year":"2014","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.conengprac.2014.10.001","article-title":"Development of misfire detection algorithm using quantitative FDI performance analysis","volume":"34","author":"Jung","year":"2015","journal-title":"Control Eng. Pract."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1016\/j.ymssp.2004.07.004","article-title":"Real-time misfire detection via sliding mode observer","volume":"19","author":"Wang","year":"2005","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/S0967-0661(98)00150-6","article-title":"Engine misfire detection","volume":"7","author":"Kiencke","year":"1999","journal-title":"Control Eng. Pract."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.neucom.2015.02.097","article-title":"Sparse Bayesian extreme learning committee machine for engine simultaneous fault diagnosis","volume":"174","author":"Wong","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Singh, S., Potala, S., and Mohanty, A.R. (2018). An improved method of detecting engine misfire by sound quality metrics of radiated sound. Proc. Inst. Mech. Eng. Part D J. Automob. Eng.","DOI":"10.1177\/0954407018818693"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.applthermaleng.2013.02.032","article-title":"Misfire detection of a turbocharged diesel engine by using artificial neural networks","volume":"55","author":"Liu","year":"2013","journal-title":"Appl. Therm. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"965","DOI":"10.4271\/2018-01-1158","article-title":"Machine Learning for Misfire Detection in a Dynamic Skip Fire Engine","volume":"11","author":"Chen","year":"2018","journal-title":"SAE Int. J. Engines"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.ymssp.2015.02.027","article-title":"Improved automated diagnosis of misfire in internal combustion engines based on simulation models","volume":"64","author":"Chen","year":"2015","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.measurement.2014.01.018","article-title":"Misfire detection in an IC engine using vibration signal and decision tree algorithms","volume":"50","author":"Sharma","year":"2014","journal-title":"Measurement"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4278","DOI":"10.1016\/j.eswa.2008.03.008","article-title":"An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network","volume":"36","author":"Wu","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1016\/j.ymssp.2010.08.010","article-title":"Multivariate statistical analysis strategy for multiple misfire detection in internal combustion engines","volume":"25","author":"Hu","year":"2011","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Szab\u00f3, J.Z., and Bakucz, P. (2018, January 13\u201315). Real-time misfire detection of large gas engine using big data analytics. Proceedings of the IEEE 16th International Symposium on Intelligent Systems and Informatics (SISY), Subotica, Serbia.","DOI":"10.1109\/SISY.2018.8524725"},{"key":"ref_24","first-page":"278","article-title":"Misfire Detection in A Multi-Cylinder Diesel Engine: A Machine Learning Approach","volume":"11","author":"Babu","year":"2016","journal-title":"J. Eng. Sci. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1016\/j.measurement.2018.04.062","article-title":"Misfire and valve clearance faults detection in the combustion engines based on a multi-sensor vibration signal monitoring","volume":"128","author":"Jafarian","year":"2018","journal-title":"Measurement"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.measurement.2018.05.098","article-title":"Multi-level wavelet packet fusion in dynamic ensemble convolutional neural network for fault diagnosis","volume":"127","author":"Han","year":"2018","journal-title":"Measurement"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1016\/j.ymssp.2013.07.009","article-title":"Fault diagnosis of diesel engine based on adaptive wavelet packets and EEMD-fractal dimension","volume":"41","author":"Wang","year":"2013","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.acha.2010.08.002","article-title":"Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool","volume":"30","author":"Daubechies","year":"2011","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1109\/TSP.2015.2391077","article-title":"Second-order synchrosqueezing transform or invertible reassignment? Towards ideal time-frequency representations","volume":"63","author":"Oberlin","year":"2015","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5441","DOI":"10.1109\/TIE.2018.2868296","article-title":"Multisynchrosqueezing transform","volume":"66","author":"Yu","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A scalable tree boosting system. Proceedings of the International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s40708-017-0065-7","article-title":"Machine learning\u2013XGBoost analysis of language networks to classify patients with epilepsy","volume":"4","author":"Torlay","year":"2017","journal-title":"Brain Inform."},{"key":"ref_33","first-page":"1","article-title":"Importance sampled learning ensembles","volume":"94305","author":"Friedman","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","article-title":"Nonlinear dimensionality reduction by locally linear embedding","volume":"290","author":"Roweis","year":"2000","journal-title":"Science"},{"key":"ref_35","first-page":"119","article-title":"Think globally, fit locally: Unsupervised learning of low dimensional manifolds","volume":"4","author":"Saul","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_36","unstructured":"Kouropteva, O., Okun, O., Hadid, A., Soriano, M., Marcos, S., and Pietik\u00e4inen, M. (2002). Beyond Locally Linear Embedding Algorithm, Springer. Technical Report MVG-01-2002."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/www.mdpi.com\/1424-8220\/19\/15\/3280\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:09:43Z","timestamp":1760188183000},"score":1,"resource":{"primary":{"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/www.mdpi.com\/1424-8220\/19\/15\/3280"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,25]]},"references-count":36,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2019,8]]}},"alternative-id":["s19153280"],"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.3390\/s19153280","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,7,25]]}}}