{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T01:27:13Z","timestamp":1778203633022,"version":"3.51.4"},"reference-count":70,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T00:00:00Z","timestamp":1676937600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Marie Sklodowvska-Curie","award":["871284"],"award-info":[{"award-number":["871284"]}]},{"name":"Marie Sklodowvska-Curie","award":["POCI-01-0145-FEDER-029494"],"award-info":[{"award-number":["POCI-01-0145-FEDER-029494"]}]},{"name":"Marie Sklodowvska-Curie","award":["PTDC\/EEI-EEE\/29494\/2017"],"award-info":[{"award-number":["PTDC\/EEI-EEE\/29494\/2017"]}]},{"name":"Marie Sklodowvska-Curie","award":["UIDB\/04131\/2020"],"award-info":[{"award-number":["UIDB\/04131\/2020"]}]},{"name":"Marie Sklodowvska-Curie","award":["UIDP\/04131\/2020"],"award-info":[{"award-number":["UIDP\/04131\/2020"]}]},{"name":"European Regional Development Fund (ERDF)","award":["871284"],"award-info":[{"award-number":["871284"]}]},{"name":"European Regional Development Fund (ERDF)","award":["POCI-01-0145-FEDER-029494"],"award-info":[{"award-number":["POCI-01-0145-FEDER-029494"]}]},{"name":"European Regional Development Fund (ERDF)","award":["PTDC\/EEI-EEE\/29494\/2017"],"award-info":[{"award-number":["PTDC\/EEI-EEE\/29494\/2017"]}]},{"name":"European Regional Development Fund (ERDF)","award":["UIDB\/04131\/2020"],"award-info":[{"award-number":["UIDB\/04131\/2020"]}]},{"name":"European Regional Development Fund (ERDF)","award":["UIDP\/04131\/2020"],"award-info":[{"award-number":["UIDP\/04131\/2020"]}]},{"name":"national funds","award":["871284"],"award-info":[{"award-number":["871284"]}]},{"name":"national funds","award":["POCI-01-0145-FEDER-029494"],"award-info":[{"award-number":["POCI-01-0145-FEDER-029494"]}]},{"name":"national funds","award":["PTDC\/EEI-EEE\/29494\/2017"],"award-info":[{"award-number":["PTDC\/EEI-EEE\/29494\/2017"]}]},{"name":"national funds","award":["UIDB\/04131\/2020"],"award-info":[{"award-number":["UIDB\/04131\/2020"]}]},{"name":"national funds","award":["UIDP\/04131\/2020"],"award-info":[{"award-number":["UIDP\/04131\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Condition-Based Maintenance (CBM), based on sensors, can only be reliable if the data used to extract information are also reliable. Industrial metrology plays a major role in ensuring the quality of the data collected by the sensors. To guarantee that the values collected by the sensors are reliable, it is necessary to have metrological traceability made by successive calibrations from higher standards to the sensors used in the factories. To ensure the reliability of the data, a calibration strategy must be put in place. Usually, sensors are only calibrated on a periodic basis; so, they often go for calibration without it being necessary or collect data inaccurately. In addition, the sensors are checked often, increasing the need for manpower, and sensor errors are frequently overlooked when the redundant sensor has a drift in the same direction. It is necessary to acquire a calibration strategy based on the sensor condition. Through online monitoring of sensor calibration status (OLM), it is possible to perform calibrations only when it is really necessary. To reach this end, this paper aims to provide a strategy to classify the health status of the production equipment and of the reading equipment that uses the same dataset. A measurement signal from four sensors was simulated, for which Artificial Intelligence and Machine Learning with unsupervised algorithms were used. This paper demonstrates how, through the same dataset, it is possible to obtain distinct information. Because of this, we have a very important feature creation process, followed by Principal Component Analysis (PCA), K-means clustering, and classification based on Hidden Markov Models (HMM). Through three hidden states of the HMM, which represent the health states of the production equipment, we will first detect, through correlations, the features of its status. After that, an HMM filter is used to eliminate those errors from the original signal. Next, an equal methodology is conducted for each sensor individually and using statistical features in the time domain where we can obtain, through HMM, the failures of each sensor.<\/jats:p>","DOI":"10.3390\/s23052402","type":"journal-article","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T02:08:34Z","timestamp":1677031714000},"page":"2402","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Online Monitoring of Sensor Calibration Status to Support Condition-Based Maintenance"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9947-0894","authenticated-orcid":false,"given":"Alexandre","family":"Martins","sequence":"first","affiliation":[{"name":"EIGeS\u2014Research Centre in Industrial Engineering, Management and Sustainability, Lus\u00f3fona University, Campo Grande 376, 1749-024 Lisboa, Portugal"},{"name":"CISE\u2014Electromechatronic Systems Research Centre, University of Beira Interior, Cal\u00e7ada Fonte do Lameiro, 62001-001 Covilh\u00e3, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0167-7489","authenticated-orcid":false,"given":"In\u00e1cio","family":"Fonseca","sequence":"additional","affiliation":[{"name":"Instituto Superior de Engenharia de Coimbra, Polytechnic of Coimbra, 3045-093 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9694-8079","authenticated-orcid":false,"given":"Jos\u00e9 Torres","family":"Farinha","sequence":"additional","affiliation":[{"name":"Instituto Superior de Engenharia de Coimbra, Polytechnic of Coimbra, 3045-093 Coimbra, Portugal"},{"name":"Centre for Mechanical Engineering, Materials and Processes\u2014CEMMPRE, University of Coimbra, 3030-788 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8504-0065","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Reis","sequence":"additional","affiliation":[{"name":"EIGeS\u2014Research Centre in Industrial Engineering, Management and Sustainability, Lus\u00f3fona University, Campo Grande 376, 1749-024 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8737-6999","authenticated-orcid":false,"given":"Ant\u00f3nio J. Marques","family":"Cardoso","sequence":"additional","affiliation":[{"name":"CISE\u2014Electromechatronic Systems Research Centre, University of Beira Interior, Cal\u00e7ada Fonte do Lameiro, 62001-001 Covilh\u00e3, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"394","DOI":"10.37394\/23203.2020.15.41","article-title":"Calibration and Certification of Industrial Sensors\u2014A Global Review","volume":"15","author":"Martins","year":"2020","journal-title":"WSEAS Trans. Syst. Control."},{"key":"ref_2","first-page":"211","article-title":"Advances in Sensors and Measurements for Metrological Applications","volume":"36","author":"Chaudhary","year":"2021","journal-title":"MAPAN J. Metrol. Soc. India"},{"key":"ref_3","unstructured":"Mateus, B., Mendes, M., Farinha, J., Martins, A., and Cardoso, A. (2023). Proceedings of IncoME-VI and TEPEN 2021, Springer."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4862","DOI":"10.1109\/TSG.2022.3204796","article-title":"Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach","volume":"13","author":"Li","year":"2022","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"33","DOI":"10.17531\/ein.2022.1.5","article-title":"Short and long forecast to implement predictive maintenance in a pulp industry","volume":"24","author":"Antunes","year":"2022","journal-title":"Eksploat. Niezawodn. Maint. Reliab."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mateus, B.C., Mendes, M., Farinha, J.T., Assis, R., and Cardoso, A.M. (2021). Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press. Energies, 14.","DOI":"10.3390\/en14216958"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2765","DOI":"10.1002\/qre.2130","article-title":"The State of the Art of Hidden Markov Models for Predictive Maintenance of Diesel Engines","volume":"33","author":"Viegas","year":"2017","journal-title":"Qual. Reliab. Eng. Int."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kou, L., Li, Y., Zhang, F., Gong, X., Hu, Y., Yuan, Q., and Ke, W. (2022). Review on Monitoring, Operation and Maintenance of Smart Offshore Wind Farms. Sensors, 22.","DOI":"10.3390\/s22082822"},{"key":"ref_9","unstructured":"Huhne, M., Krystek, M., and Odin, A. (2004). Simposio de Metrolog\u00eda, Physikalisch-Technische Bundesanstalt."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Aswal, D.K. (2020). Metrology for Inclusive Growth of India, Springer. [1st ed.].","DOI":"10.1007\/978-981-15-8872-3"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Pais, J., Raposo, H.D., Farinha, J., Cardoso, A.J.M., and Marques, P.A. (2021). Optimizing the life cycle of physical assets through an integrated life cycle assessment method. Energies, 14.","DOI":"10.3390\/en14196128"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"103632","DOI":"10.1016\/j.compind.2022.103632","article-title":"A generic interface and a framework designed for industrial metrology integration for the Internet of Things","volume":"138","author":"Sousa","year":"2022","journal-title":"Comput. Ind."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kaya, M.C., Nikoo, M.S., Schwartz, M.L., and Oguztuzun, H. (2020). Internet of Measurement Things Architecture: Proof of Concept with Scope of Accreditation. Sensors, 20.","DOI":"10.3390\/s20020503"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/S0007-8506(07)60024-9","article-title":"Productive Metrology\u2014Adding Value to Manufacture","volume":"54","author":"Kunzmann","year":"2005","journal-title":"CIRP Ann."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"109611","DOI":"10.1016\/j.measurement.2021.109611","article-title":"Towards a new generation of digital calibration certificate: Analysis and survey","volume":"181","author":"Gadelrab","year":"2021","journal-title":"Measurement"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Mester, C. (2018, January 24\u201327). The role of national metrology institutes, the International System of Units and the concept of traceability. Proceedings of the First International Colloquium on Smart Grid Metrology, Split, Croatia.","DOI":"10.23919\/SMAGRIMET.2018.8369829"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hall, B.D. (2019, January 4\u20136). An opportunity to enhance the value of metrological traceability in digital systems. Proceedings of the 2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0 IoT), Naples, Italy.","DOI":"10.1109\/METROI4.2019.8792841"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1088\/0022-3735\/20\/9\/007","article-title":"Sensors in industrial metrology","volume":"20","author":"Jones","year":"1987","journal-title":"J. Phys. Sci. Instrum."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.cie.2017.04.016","article-title":"Computers & Industrial Engineering Virtual metrology for copper-clad laminate manufacturing","volume":"109","author":"Kim","year":"2017","journal-title":"Comput. Ind. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1007\/s11018-022-02017-4","article-title":"General problems of metrology and measurement technique calibration and verification of measuring instruments: Conceptual transformation","volume":"64","author":"Levin","year":"2022","journal-title":"Meas. Tech."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"102540","DOI":"10.1016\/j.cose.2021.102540","article-title":"Identifying malicious nodes in wireless sensor networks based on correlation detection","volume":"113","author":"Lai","year":"2022","journal-title":"Comput. Secur."},{"key":"ref_22","unstructured":"Tipireddy, R., Lerchen, M., Ramuhalli, P., and Northwest, P. (2017, January 11\u201315). Virtual sensors for robust on-line monitoring (OLM) and diagnostics. Proceedings of the NPIC & HMIT 2017, San Francisco, CA, USA."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"281","DOI":"10.13182\/NT05-A3650","article-title":"Online Sensor Calibration Monitoring Uncertainty Estimation","volume":"151","author":"Hines","year":"2017","journal-title":"Nucl. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Rao, M.S., Rao, D.N., Reddy, P.C., and Shree, V.U. (2021). Fault prediction based on spatial correlation analysis using VSM in distributed sensor network. Mater. Today Proc., 2214\u20137853.","DOI":"10.1016\/j.matpr.2020.12.611"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"135266","DOI":"10.1109\/ACCESS.2021.3115819","article-title":"A Spatiotemporal and Multivariate Attribute Correlation Extraction Scheme for Detecting Abnormal Nodes in WSN","volume":"9","author":"Berjab","year":"2021","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1007\/s12206-019-0426-7","article-title":"An enhanced prediction model for the on-line monitoring of the sensors using the Gaussian process regression","volume":"33","author":"Lee","year":"2019","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"9073","DOI":"10.1109\/ACCESS.2021.3049837","article-title":"Lightweight Fault Detection Strategy for Wireless Sensor Networks Based on Trend Correlation","volume":"9","author":"Fu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.jsv.2018.10.062","article-title":"Sensor fault detection with generalized likelihood ratio and correlation coef fi cient for bridge SHM","volume":"442","author":"Li","year":"2019","journal-title":"J. Sound Vib."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"106902","DOI":"10.1016\/j.comnet.2019.106902","article-title":"Correlation analysis and statistical characterization of heterogeneous sensor data in environmental sensor networks","volume":"164","author":"Rajesh","year":"2019","journal-title":"Comput. Netw."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2573","DOI":"10.1109\/JIOT.2019.2957201","article-title":"IoT Sensor Numerical Data Trust Model Using Temporal Correlation","volume":"7","author":"Karmakar","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2649","DOI":"10.1007\/s11277-021-09257-7","article-title":"A Method for Fault Detection in Wireless Sensor Network Based on Pearson\u2019s Correlation Coefficient and Support","volume":"123","author":"Biswas","year":"2022","journal-title":"Wirel. Pers. Commun."},{"key":"ref_32","unstructured":"Hoffmann, M. (2014). PEANO\u2014A Tool for On-Line Calibration Monitoring PEANO\u2014A Tool for On-Line Calibration Monitoring, OECD Halden Reactor Project\u2014Institute for Energy Technology."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Coble, J., Ramuhalli, P., Meyer, R., Hashemian, H., Shumaker, B., and Cummins, D. (2012, January 8\u20139). Calibration Monitoring for Sensor Calibration Interval Extension: Identifying technical gaps. Proceedings of the Future of Instrumentation International Workshop (FIIW) Proceedings, Gatlinburg, TN, USA.","DOI":"10.1109\/FIIW.2012.6378348"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3766","DOI":"10.1109\/JSEN.2019.2959255","article-title":"Cooperative Cross-Correlation Algorithm to Optimize Linearity of Fused RF Sensors","volume":"20","author":"Kleber","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5323","DOI":"10.1007\/s12206-021-1105-z","article-title":"Predictive maintenance of abnormal wind turbine events by using machine learning based on condition monitoring for anomaly detection","volume":"35","author":"Chen","year":"2021","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Alqahtani, A., Ali, M., and Xie, X. (2021). Deep Time-Series Clustering: A Review. Electronics, 10.","DOI":"10.3390\/electronics10233001"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1109\/TRO.2020.3033698","article-title":"Real-Time Temporal and Rotational Calibration of Heterogeneous Sensors Using Motion Correlation Analysis","volume":"37","author":"Qiu","year":"2021","journal-title":"IEEE Trans. Robot."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.measurement.2016.05.089","article-title":"Data correlation analysis for optimal sensor placement using a bond energy algorithm","volume":"91","author":"Lu","year":"2016","journal-title":"Measurement"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Dash, L., Pattanayak, B.K., Mishra, S.K., Sahoo, K.S., Jhanjhi, N.Z., Baz, M., and Masud, M. (2022). A Data Aggregation Approach Exploiting Spatial and Temporal Correlation among Sensor Data in Wireless Sensor Networks. Electronics, 11.","DOI":"10.3390\/electronics11070989"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2651","DOI":"10.1007\/s00170-021-07021-6","article-title":"Heterogeneous sensors-based feature optimisation and deep learning for tool wear prediction","volume":"114","author":"Zhang","year":"2021","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1093\/biomet\/30.1-2.81","article-title":"A New Measure of Rank Correlation","volume":"30","author":"Kendall","year":"1938","journal-title":"Biometrika"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Awasthi, S., Travieso-Gonz\u00e1lez, C., Sanyal, G., and Singh, D. (2021). Artificial Intelligence for a Sustainable Industry 4.0, Springer.","DOI":"10.1007\/978-3-030-77070-9"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"108405","DOI":"10.1016\/j.ress.2022.108405","article-title":"Semi-supervised clustering-based method for fault diagnosis and prognosis: A case study","volume":"222","author":"Azar","year":"2022","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Martins, A., Fonseca, I., Farinha, J., Reis, J., and Cardoso, A. (2021). Maintenance prediction through sensing using hidden markov models\u2014A case study. Appl. Sci., 11.","DOI":"10.3390\/app11167685"},{"key":"ref_45","unstructured":"Schiff, A.J. (2002). Power Systems, Springer."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Rafiee, H., Aminizadeh, M., Hosseini, E.M., Aghasafari, H., and Mohammadi, A. (2022). A Cluster Analysis on the Energy Use Indicators and Carbon Footprint of Irrigated Wheat Cropping Systems. Sustainability, 14.","DOI":"10.3390\/su14074014"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Shen, X., Lin, X., and Zhang, K. (2020). Encyclopedia of Wireless Networks, Springer.","DOI":"10.1007\/978-3-319-78262-1"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Peng, K., Tan, J., and Zhang, G. (2022). A Method of Curve Reconstruction Based on Point Cloud Clustering and PCA. Symmetry, 14.","DOI":"10.3390\/sym14040726"},{"key":"ref_49","unstructured":"Satapathy, S.C., Bhateja, V., Favorskaya, M.N., and Adilakshm, T. (2021). Proceedings of the Fourth International Conference on Smart Computing and Informatics, Springer."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.biosystemseng.2020.08.004","article-title":"Model-based calibration of a gas sensor array for on-line monitoring of ethanol concentration in Saccharomyces cerevisiae batch cultivation","volume":"198","author":"Babor","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wijeratne, L.O.H., Talebi, S., and Lary, D. (2021). Machine Learning for Light Sensor Calibration. Sensors, 21.","DOI":"10.3390\/s21186259"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1007\/s00357-020-09373-2","article-title":"Using Projection-Based Clustering to Find Distance- and Density-Based Clusters in High-Dimensional Data","volume":"38","author":"Thrun","year":"2021","journal-title":"J. Classif."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.1007\/s11135-021-01176-w","article-title":"The application of K-means clustering for province clustering in Indonesia of the risk of the COVID-19 pandemic based on COVID-19 data","volume":"56","author":"Abdullah","year":"2021","journal-title":"Qual. Quant."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"128763","DOI":"10.1016\/j.jclepro.2021.128763","article-title":"A K-Sensor correlation-based evolutionary optimization algorithm to cluster contamination events and place sensors in water distribution systems","volume":"319","author":"Ali","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.neucom.2022.03.043","article-title":"Neurocomputing Improving projected fuzzy K-means clustering via robust learning","volume":"491","author":"Zhao","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Wang, J., Wang, K., Jia, R., and Chen, X. (2020, January 28\u201331). Research on Load Clustering Based on Singular Value Decomposition and K-means Clustering Algorithm. Proceedings of the 2020 Asia Energy and Electrical Engineering Symposium (AEEES), Chengdu, China.","DOI":"10.1109\/AEEES48850.2020.9121555"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1007\/s00778-021-00716-y","article-title":"Efficient exploratory clustering analyses in large-scale exploration processes","volume":"31","author":"Fritz","year":"2021","journal-title":"VLDB J."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"104743","DOI":"10.1016\/j.engappai.2022.104743","article-title":"A comprehensive survey ofclustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects","volume":"110","author":"Ezugwu","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s42162-022-00186-8","article-title":"Temperature clusters in commercial buildings using k - means and time series clustering","volume":"5","author":"Wickramasinghe","year":"2022","journal-title":"Energy Inform."},{"key":"ref_60","first-page":"63","article-title":"Improvement of K-means Cluster Quality by Post Processing Resulted Clusters","volume":"199","author":"Borlea","year":"2022","journal-title":"Int. Conf. Inf. Technol. Quant. Manag."},{"key":"ref_61","first-page":"3375","article-title":"Meta-Heuristic Optimization-Based Two-Stage Considering Intra-Cluster Compactness and Inter-Cluster Separation","volume":"56","author":"Separation","year":"2020","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Zong, P., Jiang, J., and Qin, J. (2020, January 18\u201322). Study of High-Dimensional Data Analysis based on Clustering Algorithm. Proceedings of the 2020 15th International Conference on Computer Science Education (ICCSE), Delft, The Netherlands.","DOI":"10.1109\/ICCSE49874.2020.9201656"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Rodrigues, J., Martins, A., Mendes, M., Farinha, T., Mateus, R., and Cardoso, A.J. (2022). Automatic Risk Assessment for an Industrial Asset Using. Energies, 15.","DOI":"10.3390\/en15249387"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Feng, Y., Xu, W., Zhang, Z., and Wang, F. (2022). Continuous Hidden Markov Model Based Spectrum Sensing with Estimated SNR for Cognitive UAV Networks. Sensors, 22.","DOI":"10.3390\/s22072620"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/5.18626","article-title":"A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition","volume":"77","author":"Rabiner","year":"1989","journal-title":"Proc. IEEE"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/MASSP.1986.1165342","article-title":"An Introduction to Hidden Markov Models","volume":"3","author":"Rabiner","year":"1986","journal-title":"IEEE ASSP Mag."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Martins, A., Fonseca, I., Torres, F.J., Reis, J., and Cardoso, A.J.M. (2022). Prediction Maintenance based on Vibration Analysis and Deep Learning\u2014A case study of a drying press supported on Hidden Markov Model. SSRN.","DOI":"10.2139\/ssrn.4194601"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"269","DOI":"10.3182\/20120531-2-NO-4020.00037","article-title":"On-line calibration monitoring system based on data-driven model for oil well sensors","volume":"45","author":"Boechat","year":"2012","journal-title":"IFAC Proc. Vol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"109404","DOI":"10.1016\/j.measurement.2021.109404","article-title":"Condition monitoring strategy based on an optimized selection of high-dimensional set of hybrid features to diagnose and detect multiple and combined faults in an induction motor","volume":"178","year":"2021","journal-title":"Measurement"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.measurement.2018.04.059","article-title":"Compound gear-bearing fault feature extraction using statistical features based on time-frequency method","volume":"125","author":"Dhamande","year":"2018","journal-title":"Measurement"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/5\/2402\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:38:42Z","timestamp":1760121522000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/5\/2402"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,21]]},"references-count":70,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["s23052402"],"URL":"https:\/\/doi.org\/10.3390\/s23052402","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,21]]}}}