{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T17:06:58Z","timestamp":1778605618530,"version":"3.51.4"},"reference-count":55,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,6,19]],"date-time":"2020-06-19T00:00:00Z","timestamp":1592524800000},"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":["61773160"],"award-info":[{"award-number":["61773160"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61871182"],"award-info":[{"award-number":["61871182"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["4192055"],"award-info":[{"award-number":["4192055"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The equipment condition monitoring based on computer hearing is a new pattern recognition approach, and the system formed by it has the advantages of noncontact and strong early warning abilities. Extracting effective features from the sound data of the running power equipment help to improve the equipment monitoring accuracy. However, the sound of running equipment often has the characteristics of serious noise, non-linearity and instationary, which makes it difficult to extract features. To solve this problem, a feature extraction method based on the improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and multiscale improved permutation entropy (MIPE) is proposed. Firstly, the ICEEMDAN is utilized to obtain a group of intrinsic mode functions (IMFs) from the sound of running power equipment. The noise IMFs are then identified and eliminated through mutual information (MI) and mean mutual information (meanMI) of IMFs. Next, the normalized mutual information (norMI) and MIPE are calculated respectively, and norMI is utilized to weigh the corresponding MIPE result. Finally, based on the separability criterion, the weighted MIPE results are feature-dimensionally reduced to obtain the multiscale entropy feature of the sound. The experimental results show that the classification accuracies of the method under the conditions of no noise and 5 dB reach 96.7% and 89.9%, respectively. In practice, the proposed method has higher reliability and stability for the sound feature extraction of the running power equipment.<\/jats:p>","DOI":"10.3390\/e22060685","type":"journal-article","created":{"date-parts":[[2020,6,19]],"date-time":"2020-06-19T10:43:58Z","timestamp":1592563438000},"page":"685","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Multiscale Entropy Feature Extraction Method of Running Power Equipment Sound"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2997-5840","authenticated-orcid":false,"given":"Yongjie","family":"Zhai","sequence":"first","affiliation":[{"name":"Department of Automation, North China Electricity Power University, Baoding 071003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Automation, North China Electricity Power University, Baoding 071003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yani","family":"Peng","sequence":"additional","affiliation":[{"name":"Department of Automation, North China Electricity Power University, Baoding 071003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinying","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer, North China Electricity Power University, Baoding 071003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kang","family":"Bai","sequence":"additional","affiliation":[{"name":"Department of Automation, North China Electricity Power University, Baoding 071003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Basak, D., Tiwari, A., and Das, S. (2006, January 15\u201317). Fault diagnosis and condition monitoring of electrical machines-A Review. Proceedings of the 2006 IEEE International Conference on Industrial Technology, Mumbai, India.","DOI":"10.1109\/ICIT.2006.372719"},{"key":"ref_2","first-page":"24","article-title":"Review of Research on State Monitoring of Electrical Equipment Based on Computer Hearing","volume":"32","author":"Zhai","year":"2019","journal-title":"Guangdong Electr. Power"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zheng, Z., and Xin, G. (2019). Fault Feature Extraction of Hydraulic Pumps Based on Symplectic Geometry Mode Decomposition and Power Spectral Entropy. Entropy, 21.","DOI":"10.3390\/e21050476"},{"key":"ref_4","first-page":"67","article-title":"Multi-dimensional state monitoring and fault diagnosis of 500 kV Transformer","volume":"21","author":"Zhang","year":"2018","journal-title":"Power Syst. Big Data"},{"key":"ref_5","first-page":"62","article-title":"Discussion on Online Monitoring and Condition Based Maintenance Technology for Power Primary Equipment","volume":"35","author":"Yang","year":"2018","journal-title":"Telecom Power Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"78706","DOI":"10.1109\/ACCESS.2019.2922257","article-title":"Texture-and-shape based active contour model for insulator segmentation","volume":"7","author":"Yu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Pernebayeva, D., Bagheri, M., and James, A. (2017, January 11\u201315). High voltage insulator surface evaluation using image processing. Proceedings of the 2017 International Symposium on Electrical Insulating Materials(ISEIM), Toyohashi, Japan.","DOI":"10.23919\/ISEIM.2017.8166540"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"41590","DOI":"10.1109\/ACCESS.2018.2859048","article-title":"A Deep Learning Approach for Oriented Electrical Equipment Detection in Thermal Images","volume":"6","author":"Gong","year":"2018","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Vasile, C., and Ioana, C. (2016, January 10\u201313). Arc fault detection & localization by electromagnetic-acoustic remote sensing. Proceedings of the IOP Conference Series: Materials Science and Engineering, St. Gilles-les-Bains, Reunion.","DOI":"10.1088\/1757-899X\/198\/1\/012009"},{"key":"ref_10","first-page":"115","article-title":"Research on GIS bus fault location based on pulse voltage and ultrasonic detection","volume":"32","author":"Du","year":"2019","journal-title":"Guangdong Electr. Power"},{"key":"ref_11","first-page":"71","article-title":"Application of ultrasonic testing technology in insulation diagnosis of power equipment","volume":"12","author":"Zhang","year":"2017","journal-title":"Autom. Instrum."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.triboint.2010.09.008","article-title":"Numerical study of the thermo-hydrodynamic lubrication phenomena in porous journal bearings","volume":"44","author":"Boubendir","year":"2011","journal-title":"Tribol. Int."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/j.triboint.2009.07.006","article-title":"Polymer gear surface thermal wear and its performance prediction","volume":"43","author":"Mao","year":"2010","journal-title":"Tribol. Int."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"11178","DOI":"10.1109\/ACCESS.2019.2892601","article-title":"On-line monitoring of partial discharge of less-oil immersed electric equipment based on pressure and UHF","volume":"7","author":"Guozhi","year":"2019","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Balan, H., Varodi, T., Buzdugan, M.I., and Munteanu, R.A. (2016, January 6\u20138). Monitoring power breakers using vibro acoustic techniques. In Proceeding of the 2016 International Conference on Applied and Theoretical Electricity (ICATE 2016), Craiova, Romania.","DOI":"10.1109\/ICATE.2016.7754664"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Long, X., Yang, P., Guo, H., Zhao, Z., and Wu, X. (2019). A CBA-KELM-based recognition method for fault diagnosis of wind turbines with time-domain analysis and multisensor data fusion. Shock Vib., 2019.","DOI":"10.1155\/2019\/7490750"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yang, F., Kou, Z., Wu, J., and Li, T. (2018). Application of mutual information-sample entropy based MED-ICEEMDAN De-noising scheme for weak fault diagnosis of hoist bearing. Entropy, 20.","DOI":"10.3390\/e20090667"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1109\/MSP.2010.937498","article-title":"Machine hearing: An emerging field [exploratory dsp]","volume":"27","author":"Lyon","year":"2010","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zaporozhets, A., Eremenko, V., Serhiienko, R., and Ivanov, S. (2018, January 11\u201314). Development of an intelligent system for diagnosing the technical condition of the heat power equipment. Proceedings of the 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), Lviv, Ukraine.","DOI":"10.1109\/STC-CSIT.2018.8526742"},{"key":"ref_20","first-page":"1137","article-title":"Noise measurement and analysis of the main power apparatus in a 500 kV substation","volume":"34","author":"Wu","year":"2016","journal-title":"Environ. Eng."},{"key":"ref_21","first-page":"86","article-title":"Fault Analysis of Construction Machinery Hydraulic System Based on Noise","volume":"33","author":"Wu","year":"2012","journal-title":"J. North China Inst. Water Conserv. Hydroelectr. Power"},{"key":"ref_22","first-page":"154","article-title":"Noise Analysis and Signal Processing in the Turbine Valve Closing Test","volume":"52","author":"Mei","year":"2019","journal-title":"Electr. Power"},{"key":"ref_23","first-page":"1","article-title":"Preliminary Study on Cracking Defect Detection of the Fan Blades based on Aerodynamic Noise","volume":"34","author":"Yan","year":"2018","journal-title":"Instrum. Anal. Monit."},{"key":"ref_24","first-page":"918","article-title":"Design and Implementation of Corona Audible Noise Signal Detecting System of High Voltage Transmission Line","volume":"40","author":"Wang","year":"2017","journal-title":"Chin. J. Electron. Devices"},{"key":"ref_25","first-page":"25","article-title":"A Diagnosis Method for Hydraulic Generator Fault Based on Noise Frequency Band Extraction","volume":"45","author":"Hu","year":"2017","journal-title":"Large Electr. Mach. Hydraul. Turbine"},{"key":"ref_26","first-page":"69","article-title":"Fault Diagnosis of Broken Rotor Bars of Asynchronous Motors Based on Stator Winding Signal Current and Noise Signal","volume":"42","author":"Wei","year":"2015","journal-title":"Electr. Mach. Control Appl."},{"key":"ref_27","first-page":"18","article-title":"Characteristic analysis of transformer audible acoustic signals","volume":"21","author":"Ma","year":"2018","journal-title":"Power Syst. Big Data"},{"key":"ref_28","first-page":"162","article-title":"Fan\u2019s Fault Diagnosis by Noise Frequency Spectrum","volume":"25","author":"Cheng","year":"2002","journal-title":"Nondestruct. Test."},{"key":"ref_29","first-page":"37","article-title":"Wind Turbine Blade Damage Identification Based on Harmonic Wavelet Packet and Support Vector Machine","volume":"41","author":"Rao","year":"2014","journal-title":"Fiber Reinf. Plast.\/Compos."},{"key":"ref_30","first-page":"44","article-title":"Method to Eliminate Noise in On-Line PD Monitoring System of Transformer Based on Wavelet Analysis","volume":"42","author":"Fu","year":"2005","journal-title":"Transformer"},{"key":"ref_31","first-page":"81","article-title":"The Application of Wavelet Packet Filter in GIS Partial Discharge On-line Monitoring System","volume":"8","author":"Shen","year":"2017","journal-title":"Digit. Technol. Appl."},{"key":"ref_32","first-page":"2603","article-title":"Noise Diagnosis Method of Distribution Transformer Discharge Fault Based on CEEMDAN","volume":"44","author":"Shu","year":"2018","journal-title":"High Volt. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1098\/rspa.1998.0193","article-title":"The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis","volume":"454","author":"Huang","year":"1998","journal-title":"Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1142\/S1793536909000047","article-title":"Ensemble empirical mode decomposition: A noise-assisted data analysis method","volume":"1","author":"Wu","year":"2009","journal-title":"Adv. Adapt. Data Anal."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1142\/S1793536910000422","article-title":"Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method","volume":"2","author":"Yeh","year":"2010","journal-title":"Adv. Adapt. Data Anal."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"132492","DOI":"10.1109\/ACCESS.2019.2941497","article-title":"Fault Diagnosis of Gas Pressure Regulators Based on CEEMDAN and Feature Clustering","volume":"7","author":"Tian","year":"2019","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"60091","DOI":"10.1109\/ACCESS.2019.2915252","article-title":"Mechanical faults diagnosis of high-voltage circuit breaker via hybrid features and integrated extreme learning machine","volume":"7","author":"Gao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_38","first-page":"181","article-title":"Mechanical fault diagnosis of high voltage circuit breaker based on CEEMDAN sample entropy and FWA-SVM","volume":"40","author":"Zhao","year":"2020","journal-title":"Electr. Power Autom. Equip."},{"key":"ref_39","first-page":"95","article-title":"CEEMDAN adaptive threshold denoising algorithm in application to seismic direction","volume":"42","author":"Liu","year":"2019","journal-title":"J. Chongqing Univ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"42042","DOI":"10.1109\/ACCESS.2020.2977219","article-title":"A Hybrid Model for Lane-Level Traffic Flow Forecasting Based on Complete Ensemble Empirical Mode Decomposition and Extreme Gradient Boosting","volume":"8","author":"Lu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"159754","DOI":"10.1109\/ACCESS.2019.2950798","article-title":"Cardiotocographic Signal Feature Extraction Through CEEMDAN and Time-Varying Autoregressive Spectral-Based Analysis for Fetal Welfare Assessment","volume":"7","author":"Fuentealba","year":"2019","journal-title":"IEEE Access"},{"key":"ref_42","first-page":"1211","article-title":"Electroencephalogram Artifact Filtering Method of Single Channel EEG Based on CEEMDAN-ICA","volume":"31","author":"Luo","year":"2018","journal-title":"Chin. J. Sens. Actuators"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Torres, M.E., Colominas, M.A., Schlotthauer, G., and Flandrin, P. (2011, January 22\u201327). A complete ensemble empirical mode decomposition with adaptive noise. Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011), Prague, Czech Republic.","DOI":"10.1109\/ICASSP.2011.5947265"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","article-title":"Physiological time-series analysis using approximate entropy and sample entropy","volume":"278","author":"Richman","year":"2000","journal-title":"Am. J. Physiol.-Heart Circ. Physiol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.medengphy.2008.04.005","article-title":"Measuring complexity using fuzzyen, apen, and sampen","volume":"31","author":"Chen","year":"2009","journal-title":"Med. Eng. Phys."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"174102","DOI":"10.1103\/PhysRevLett.88.174102","article-title":"Permutation entropy: A natural complexity measure for time series","volume":"88","author":"Bandt","year":"2002","journal-title":"Phys. Rev. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.ymssp.2017.12.008","article-title":"A fault diagnosis scheme for planetary gearboxes using adaptive multi-scale morphology filter and modified hierarchical permutation entropy","volume":"105","author":"Li","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chen, Z., Li, Y., Liang, H., and Yu, J. (2019). Improved permutation entropy for measuring complexity of time series under noisy condition. Complexity, 2019.","DOI":"10.1155\/2019\/1403829"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"021906","DOI":"10.1103\/PhysRevE.71.021906","article-title":"Multiscale entropy analysis of biological signals","volume":"71","author":"Costa","year":"2005","journal-title":"Phys. Rev. E"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Kv\u00e5lseth, T.O. (2017). On normalized mutual information: Measure derivations and properties. Entropy, 19.","DOI":"10.3390\/e19110631"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"015117","DOI":"10.1063\/1.2404630","article-title":"An integrated approach based on uniform quantization for the evaluation of complexity of short-term heart period variability: Application to 24 h Holter recordings in healthy and heart failure humans","volume":"17","author":"Porta","year":"2007","journal-title":"Chaos Interdiscip. J. Nonlinear Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1049\/el:20000458","article-title":"Feature selection for multi-class classification using pairwise class discriminatory measure and covering concept","volume":"36","author":"Ji","year":"2000","journal-title":"Electron. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Zhao, H., Sun, M., Deng, W., and Yang, X. (2017). A new feature extraction method based on EEMD and multi-scale fuzzy entropy for motor bearing. Entropy, 19.","DOI":"10.3390\/e19010014"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Du, W., Guo, X., Wang, Z., Wang, J., Yu, M., Li, C., Wang, G., Wang, L., Guo, H., and Zhou, J. (2020). A New Fuzzy Logic Classifier Based on Multiscale Permutation Entropy and Its Application in Bearing Fault Diagnosis. Entropy, 22.","DOI":"10.3390\/e22010027"},{"key":"ref_55","first-page":"1","article-title":"CEEMDAN-Based Permutation Entropy: A Suitable Feature for the Fault Identification of Spiral-Bevel Gears","volume":"2019","author":"Jiang","year":"2019","journal-title":"Shock Vib."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/6\/685\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:40:49Z","timestamp":1760175649000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/6\/685"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,19]]},"references-count":55,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["e22060685"],"URL":"https:\/\/doi.org\/10.3390\/e22060685","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,19]]}}}