{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T19:44:44Z","timestamp":1772653484030,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,7,30]],"date-time":"2023-07-30T00:00:00Z","timestamp":1690675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61671338"],"award-info":[{"award-number":["61671338"]}]},{"name":"National Natural Science Foundation of China","award":["2023C0204"],"award-info":[{"award-number":["2023C0204"]}]},{"name":"Wuhan University of Science and Technology","award":["61671338"],"award-info":[{"award-number":["61671338"]}]},{"name":"Wuhan University of Science and Technology","award":["2023C0204"],"award-info":[{"award-number":["2023C0204"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>An escalator is an essential large-scale public transport equipment; once it fails, this inevitably affects the operation of the escalator and even leads to safety concerns, or perhaps accidents. As an important structural part of the escalator, the foundation of the main engine can cause the operation of the escalator to become abnormal when its fixing bolts become loose. Aiming to reduce the difficulty of extracting the fault features of the footing bolt when it loosens, a fault feature extraction method is proposed in this paper based on empirical wavelet transform (EWT) and the gray-gradient co-occurrence matrix (GGCM). Firstly, the Teager energy operator and multi-scale peak determination are used to improve the spectral partitioning ability of EWT, and the improved EWT is used to decompose the original foundation vibration signal into a series of empirical mode functions (EMFs). Then, the gray-gradient co-occurrence matrix of each EMF is constructed, and six texture features of the gray-gradient co-occurrence matrix are calculated as the fault feature vectors of this EMF. Finally, the fault features of all EMFs are fused, and the degree of the loosening of the escalator foundation bolt is identified using the fused multi-scale feature vector and BiLSTM. The experimental results show that the proposed method based on EWT and GGCM feature extraction can diagnose the loosening degree of foundation bolts more effectively and has a certain engineering application value.<\/jats:p>","DOI":"10.3390\/s23156801","type":"journal-article","created":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T03:30:02Z","timestamp":1690774202000},"page":"6801","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Escalator Foundation Bolt Loosening Fault Recognition Based on Empirical Wavelet Transform and Multi-Scale Gray-Gradient Co-Occurrence Matrix"],"prefix":"10.3390","volume":"23","author":[{"given":"Xuezhuang","family":"E","sequence":"first","affiliation":[{"name":"Hubei Province Key Laboratory of System Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan 430081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2130-7672","authenticated-orcid":false,"given":"Wenbo","family":"Wang","sequence":"additional","affiliation":[{"name":"Hubei Province Key Laboratory of System Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan 430081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,30]]},"reference":[{"key":"ref_1","first-page":"2725","article-title":"Study on the cause and prevention of subway escalator passenger injury accident","volume":"5","author":"Liu","year":"2011","journal-title":"Chin. Railw."},{"key":"ref_2","first-page":"10720745","article-title":"High frequency demodulation technique for instantaneous angular speed estimation","volume":"152","author":"Bonnardot","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"111554","DOI":"10.1016\/j.engstruct.2020.111554","article-title":"Evaluation of a pendulum pounding tuned mass damper for seismic control of structures","volume":"228","author":"Wang","year":"2021","journal-title":"Eng. Struct."},{"key":"ref_4","first-page":"51","article-title":"Research on fault diagnosis method for main drive shaft bearing of escalator based EEMD-SVM","volume":"38","author":"Meng","year":"2020","journal-title":"Mach. Electron."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1177\/16878140211001963","article-title":"Research on measuring device and quantifiable risk assessment method based on FMEA of escalator brake","volume":"13","author":"Ma","year":"2021","journal-title":"Adv. Mech. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"116026","DOI":"10.1016\/j.jsv.2021.116026","article-title":"Extended Empical Wavelet Transformation: Application to structural updating","volume":"500","author":"Karimpour","year":"2021","journal-title":"J. Sound Vib."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1007\/s42417-021-00316-8","article-title":"Sensitive Sub-band Selection Criteria for Empirical Wavelet Transform to Detect Bearing Fault Based on Vibration Signals","volume":"9","author":"Sharma","year":"2021","journal-title":"J. Vib. Eng. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chui, K.T., Gupta, B.B., Liu, R.-W., and Vasant, P. (2021). Handling Data Heterogeneity in Electricity Load Disaggregation via Optimized Complete Ensemble Empirical Mode Decomposition and Wavelet Packet Transform. Sensors, 21.","DOI":"10.3390\/s21093133"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1142\/S0219455421500462","article-title":"Time-Varying Parameter Identification of Bridges Subject to Moving Vehicles Using Ridge Extraction Based on Empirical Wavelet Transform","volume":"21","author":"Li","year":"2021","journal-title":"Int. J. Struct. Stab. Dyn."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.ymssp.2021.107817","article-title":"Fault diagnosis of rolling bearing based on empirical mode decomposition and improved manhattan distance in symmetrized dot pattern image","volume":"159","author":"Sun","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1080\/00150193.2020.1760619","article-title":"An adaptive empirical mode decomposition and stochastic resonance system in high efficient detection of terahertz radar signal","volume":"563","author":"Wang","year":"2020","journal-title":"Ferroelectrics"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1016\/j.bbe.2020.05.007","article-title":"Detection of SSVEP based on empirical mode decomposition and power spectrum peaks analysis","volume":"40","author":"Antelis","year":"2020","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"107966","DOI":"10.1016\/j.apacoust.2021.107966","article-title":"Application of adaptive complementary ensemble local mean decomposition in underwater acoustic signal processin","volume":"178","author":"Lu","year":"2021","journal-title":"Appl. Acoust."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"012076","DOI":"10.1088\/1755-1315\/69\/1\/012076","article-title":"Fault diagnosis of automaton based on local characteristic-scale decomposition and individual feature selection","volume":"69","author":"Du","year":"2017","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_15","first-page":"4077","article-title":"Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network","volume":"107","author":"Toma","year":"2020","journal-title":"Electronics"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4689","DOI":"10.1016\/j.aej.2021.03.034","article-title":"Applicationof variational mode decomposition optimized with improved whale optimization algorithm in bearing failure diagnosis","volume":"60","author":"Wang","year":"2021","journal-title":"Alex. Eng. J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"114587","DOI":"10.1016\/j.eswa.2021.114587","article-title":"A multiverse optimization based colour image segmentation using variational mode decomposition","volume":"171","author":"Chouksey","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yan, X.-Y., Liu, Y., Zhang, W., Jia, M.-P., and Wang, X.-B. (2020). Research on a NovelImproved Adaptive Variational Mode Decomposition Method in Rotor Fault Diagnosis. Appl. Sci., 10.","DOI":"10.3390\/app10051696"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1109\/LGRS.2019.2947220","article-title":"Seismic Reservoir Delineation via Hankel Transform Based Enhanced Empirical Wavelet Transform","volume":"17","author":"Li","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"109135","DOI":"10.1016\/j.measurement.2021.109135","article-title":"A double impulsiveness measurement indices-bilaterally driven empirical wavelet transform and its application to wheelset-bearing-system compound fault detection","volume":"175","author":"Ding","year":"2021","journal-title":"Measurement"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"108976","DOI":"10.1016\/j.measurement.2021.108976","article-title":"Feature extraction method based on adaptive and concise empirical wavelet transform and its applications in bearing fault diagnosis","volume":"172","author":"Zhang","year":"2021","journal-title":"Measurement"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/JPHOT.2020.2992135","article-title":"WT-ASG: Empirical Wavelet Transform With Adaptive Savitzky-Golay Filtering for TDLAS","volume":"12","author":"He","year":"2020","journal-title":"IEEE Photonics J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"114568","DOI":"10.1016\/j.eswa.2021.114568","article-title":"An expert system for EMI data classification based on complex Bispectrum representation and deep learning methods","volume":"171","author":"Mitiche","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"035204","DOI":"10.1088\/1361-6501\/abc3df","article-title":"Surface crack characterization using laser nonlinear ultrasonics based on the bispectrum","volume":"32","author":"Liu","year":"2021","journal-title":"Meas. Sci. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"8823389","DOI":"10.1155\/2020\/8823389","article-title":"Fault Characteristic Extraction by Fractional Lower-Order Bispectrum Methods","volume":"2020","author":"Wang","year":"2020","journal-title":"Math. Probl. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1177\/1475921720949827","article-title":"A phase linearisation-based modulation signal bispectrum for analysing cyclostationary bearing signals","volume":"20","author":"Xu","year":"2021","journal-title":"Struct. Health Monit. Int. J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2517","DOI":"10.1007\/s42835-023-01547-3","article-title":"Advanced Phase-Difference Estimation Algorithm Using Cross Teager\u2013Kai ser Energy Operator for DERs Synchronization","volume":"18","author":"Lee","year":"2023","journal-title":"J. Electr. Eng. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"212","DOI":"10.3390\/s21010212","article-title":"Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine","volume":"21","author":"Chen","year":"2021","journal-title":"Sensors"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Suthar, V., Vakharia, V., Patel, V.K., and Shah, M. (2023). Detection of Compound Faults in Ball Bearings Using Multiscale-SinGAN, Heat Transfer Search Optimization, and Extreme Learning Machine. Machines, 11.","DOI":"10.3390\/machines11010029"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"105110","DOI":"10.1088\/1361-6501\/ac7635","article-title":"Rolling bearing fault diagnosis method based on SSAE and softmax classifier with improved K-fold cross-validation","volume":"33","author":"Wang","year":"2022","journal-title":"Meas. Sci. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.ast.2017.06.008","article-title":"Multi objective optimization of sound transmission across laminated composite cylindrical shell lined with porous core investigating Non-dominated Sorting Genetic Algorithm","volume":"69","author":"Talebitooti","year":"2017","journal-title":"Aerosp. Sci. Technol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/15\/6801\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:22:33Z","timestamp":1760127753000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/15\/6801"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,30]]},"references-count":31,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["s23156801"],"URL":"https:\/\/doi.org\/10.3390\/s23156801","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,30]]}}}