{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T13:03:48Z","timestamp":1769346228877,"version":"3.49.0"},"reference-count":23,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2019,9,20]],"date-time":"2019-09-20T00:00:00Z","timestamp":1568937600000},"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":["51805116"],"award-info":[{"award-number":["51805116"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The spindle box is responsible for power transmission, supporting the rotating parts and ensuring the rotary accuracy of the workpiece in the heavy-duty machine tool. Its assembly quality is crucial to ensure the reliable power supply and stable operation of the machine tool in the process of large load and cutting force. Therefore, accurate diagnosis of assembly faults is of great significance for improving assembly efficiency and ensuring outgoing quality. In this paper, the common fault types and characteristics of the spindle box of heavy horizontal lathe are analyzed first, and original vibration signals of various fault types are collected. The wavelet packet is used to decompose the signal into different frequency bands and reconstruct the nodes in the frequency band where the characteristic frequency points are located. Then, the power spectrum analysis is carried out on the reconstructed signal, so that the fault features in the signal can be clearly expressed. The structure of the feature vector used for fault diagnosis is analyzed and the feature vector is extracted from the collected signals. Finally, the intelligent pattern recognition method based on support vector machine is used to classify the fault types. The results show that the method proposed in this paper can quickly and accurately judge the fault types.<\/jats:p>","DOI":"10.3390\/s19194069","type":"journal-article","created":{"date-parts":[[2019,9,20]],"date-time":"2019-09-20T10:48:14Z","timestamp":1568976494000},"page":"4069","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Fault Diagnosis of Rotary Parts of a Heavy-Duty Horizontal Lathe Based on Wavelet Packet Transform and Support Vector Machine"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6950-773X","authenticated-orcid":false,"given":"Hongyu","family":"Jin","sequence":"first","affiliation":[{"name":"School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Avitus","family":"Titus","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China"},{"name":"Department of Engineering Sciences and Technology, Sokoine University of Agriculture, Morogoro 255, Tanzania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yulong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenyu","family":"Han","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xu, G.W., Liu, M., Jiang, Z.F., S\u00f6ffker, D., and Shen, W.M. (2019). Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning. Sensors, 19.","DOI":"10.3390\/s19051088"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1109\/JSEN.2017.2771226","article-title":"Fault detection in Wireless Sensor Networks through SVM classifier","volume":"18","author":"Zidi","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Li, Y.X., Zhao, W., Li, Q.S., Wang, T.C., and Wang, G. (2019). In-Situ Monitoring and Diagnosing for Fused Filament Fabrication Process Based on Vibration Sensors. Sensors, 19.","DOI":"10.3390\/s19112589"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2731","DOI":"10.1007\/s12206-011-0717-0","article-title":"Bearing fault diagnosis based on amplitude and phase map of Hermitian wavelet transform","volume":"25","author":"Li","year":"2011","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.ymssp.2004.03.008","article-title":"Fault diagnosis of rolling element bearings using basis pursuit","volume":"19","author":"Yang","year":"2005","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2862","DOI":"10.1016\/j.renene.2010.05.012","article-title":"Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution","volume":"35","author":"Tang","year":"2010","journal-title":"Renew. Energy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1016\/j.ymssp.2005.02.003","article-title":"Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum","volume":"20","author":"Liu","year":"2006","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Xu, F.J., and Ma, T.H. (2019). Modeling and Studying Acceleration-Induced Effects of Piezoelectric Pressure Sensors Using System Identification Theory. Sensors, 19.","DOI":"10.3390\/s19051052"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Guo, J.C., Shi, Z.Q., Li, H.Y., Zhen, D., Gu, F.S., and Ball, A.D. (2018). Early Fault Diagnosis for Planetary Gearbox Based Wavelet Packet Energy and Modulation Signal Bispectrum Analysis. Sensors, 18.","DOI":"10.3390\/s18092908"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1006\/jsvi.1996.0226","article-title":"Application of wavelets to gearbox vibration signals for fault detection","volume":"192","author":"Wang","year":"1996","journal-title":"J. Sound Vib."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1016\/j.ndteint.2005.04.003","article-title":"Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition","volume":"38","author":"Purushotham","year":"2005","journal-title":"NDT E Int."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1746","DOI":"10.1016\/j.ymssp.2006.08.005","article-title":"Intelligent condition monitoring of a gearbox using artificial neural network","volume":"21","author":"Rafiee","year":"2007","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1016\/S0888-3270(03)00020-7","article-title":"Gear fault detection using artificial neural networks and support vector machines with genetic algorithms","volume":"18","author":"Samanta","year":"2004","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1278","DOI":"10.1109\/TMI.2005.855435","article-title":"Relevance vector machine for automatic detection of clustered microcalcifications","volume":"24","author":"Wei","year":"2005","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/S0888-3270(03)00076-1","article-title":"Hidden Markov model based fault diagnosis for stamping processes","volume":"18","author":"Ge","year":"2004","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Cen, Y., Cen, Y.G., Wang, K., and Li, J.C. (2019). Energy-Efficient Nonuniform Content Edge Pre-Caching to Improve Quality of Service in Fog Radio Access Networks. Sensors, 19.","DOI":"10.3390\/s19061422"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, Z.X., Pan, S.G., Gao, C.F., Zhao, T., and Gao, W. (2019). Support Vector Machine for Regional Ionospheric Delay Modeling. Sensors, 19.","DOI":"10.3390\/s19132947"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"77121","DOI":"10.18632\/oncotarget.20365","article-title":"MLACP: Machine-learning-based prediction of anticancer peptides","volume":"8","author":"Manavalan","year":"2017","journal-title":"Oncotarget"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1016\/j.omtn.2019.04.019","article-title":"Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation","volume":"16","author":"Manavalan","year":"2019","journal-title":"Mol. Ther.-Nucleic Acids"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wei, L.Y., Su, R., Luan, S.S., Liao, Z.J., Manavalan, B., Zou, Q., and Shi, X.L. (2019). Iterative feature representations improve N4-methylcytosine site prediction. Bioinformatics, btz408.","DOI":"10.1093\/bioinformatics\/btz408"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2496","DOI":"10.1093\/bioinformatics\/btx222","article-title":"SVMQA: Support-vector-machine-based protein single-model quality assessment","volume":"33","author":"Manavalan","year":"2017","journal-title":"Bioinformatics"},{"key":"ref_22","first-page":"89","article-title":"Fault Pattern Recognition of Rolling Bearing Based on EMD-SVD Model and SVM","volume":"2","author":"Wu","year":"2011","journal-title":"Noise Vib. Control"},{"key":"ref_23","first-page":"2184","article-title":"Gear fault diagnosis based on SVM and multi-sensor information fusion","volume":"41","author":"Jiang","year":"2010","journal-title":"J. Cent. South Univ. (Sci. Technol.)"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4069\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:22:29Z","timestamp":1760188949000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4069"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,20]]},"references-count":23,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["s19194069"],"URL":"https:\/\/doi.org\/10.3390\/s19194069","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,20]]}}}