{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T23:10:03Z","timestamp":1775085003178,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,7,17]],"date-time":"2018-07-17T00:00:00Z","timestamp":1531785600000},"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":["No. 61201306 and 61327804"],"award-info":[{"award-number":["No. 61201306 and 61327804"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Metal Oxide Semiconductor (MOS) gas sensor has been widely used in sensor systems for the advantages of fast response, high sensitivity, low cost, and so on. But, limited to the properties of materials, the phenomenon, such as aging, poisoning, and damage of the gas sensitive material will affect the measurement quality of MOS gas sensor array. To ensure the stability of the system, a health management decision strategy for the prognostics and health management (PHM) of a sensor system that is based on health reliability degree (HRD) and grey group decision-making (GGD) is proposed in this paper. The health management decision-making model is presented to choose the best health management strategy. Specially, GGD is utilized to provide health management suggestions for the sensor system. To evaluate the status of the sensor system, a joint HRD-GGD framework is declared as the health management decision-making. In this method, HRD of sensor system is obtained by fusing the output data of each sensor. The optimal decision-making recommendations for health management of the system is proposed by combining historical health reliability degree, maintenance probability, and overhaul rate. Experimental results on four different kinds of health levels demonstrate that the HRD-GGD method outperforms other methods in decision-making accuracy of sensor system. Particularly, the proposed HRD-GGD decision-making method achieves the best decision accuracy of 98.25%.<\/jats:p>","DOI":"10.3390\/s18072316","type":"journal-article","created":{"date-parts":[[2018,7,18]],"date-time":"2018-07-18T08:45:42Z","timestamp":1531903542000},"page":"2316","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Health Management Decision of Sensor System Based on Health Reliability Degree and Grey Group Decision-Making"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4330-6985","authenticated-orcid":false,"given":"Kai","family":"Song","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Peng","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Guo","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Yinsheng","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Measurement and Communication Engineering, Harbin University of Science and Technology, Harbin 150001, China"}]},{"given":"Qi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,7,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.snb.2013.03.034","article-title":"Algorithmic mitigation of sensor failure: Is sensor replacement really necessary?","volume":"183","author":"Fonollosa","year":"2013","journal-title":"Sens. Actuators B Chem."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1016\/j.snb.2013.07.101","article-title":"Engineering approaches for the improvement of conductometric gas sensor parameters: Part 1. Improvement of sensor sensitivity and selectivity (short survey)","volume":"188","author":"Korotcenkov","year":"2013","journal-title":"Sens. Actuators B Chem."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.snb.2014.03.069","article-title":"Engineering approaches to improvement of conductometric gas sensor parameters. Part 2: Decrease of dissipated. (consumable) power and improvement stability and reliability","volume":"198","author":"Korotcenkov","year":"2014","journal-title":"Sens. Actuators B Chem."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1016\/j.proeng.2014.11.287","article-title":"Robustness to Sensor Damage of a Highly Redundant Gas Sensor Array","volume":"87","author":"Fernandez","year":"2014","journal-title":"Procedia Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/S0925-4005(02)00036-9","article-title":"Three years experiment with the same tin oxide sensor arrays for the identification of malodorous sources in the environment","volume":"84","author":"Romain","year":"2002","journal-title":"Sens. Actuators B Chem."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.snb.2009.12.027","article-title":"Long term stability of metal oxide-based gas sensors for e-nose environmental applications: An overview","volume":"146","author":"Romain","year":"2009","journal-title":"Sens. Actuators B Chem."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"9481","DOI":"10.3390\/s150409481","article-title":"Design and Field Test of a WSN Platform Prototype for Long-Term Environmental Monitoring","volume":"15","author":"Lazarescu","year":"2015","journal-title":"Sensors"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"04017114","DOI":"10.1061\/(ASCE)CF.1943-5509.0001101","article-title":"Analytic Hierarchy Process-Simulation Framework for Lighting Maintenance Decision-Making Based on the Clustered Network","volume":"32","author":"Chen","year":"2018","journal-title":"J. Perform. Constr. Facil."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"061008","DOI":"10.1115\/1.4039197","article-title":"Demand Response Driven Production and Maintenance Decision Making for Cost Effective Manufacturing","volume":"140","author":"Dababneh","year":"2018","journal-title":"J. Manuf. Sci. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.ress.2004.11.001","article-title":"Optimization for condition-based maintenance with semi-Markov decision process","volume":"90","author":"Chen","year":"2017","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2015\/793161","article-title":"Prognostics and health management: A review on data driven approaches","volume":"6","author":"Tsui","year":"2015","journal-title":"Math. Probl. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.jpowsour.2014.12.050","article-title":"A self-cognizant dynamic system approach for prognostics and health management","volume":"278","author":"Bai","year":"2015","journal-title":"J. Power Sources"},{"key":"ref_13","first-page":"1","article-title":"A review of prognostics and health management applications in nuclear power plants","volume":"6","author":"Coble","year":"2015","journal-title":"Int. J. Progn. Health Manag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"10109","DOI":"10.3390\/s120810109","article-title":"A Monotonic Degradation Assessment Index of Rolling Bearings Using Fuzzy Support Vector Data Description and Running Time","volume":"12","author":"Shen","year":"2012","journal-title":"Sensors"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Sohaib, M., Kim, C.H., and Kim, J.M. (2017). A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis. Sensors, 17.","DOI":"10.3390\/s17122876"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, J., Wang, X., and Wang, L. (2017). Modeling of BN Lifetime Prediction of a System Based on Integrated Multi-Level Information. Sensors, 17.","DOI":"10.3390\/s17092123"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Agarwal, V., Lybeck, N.J., Bickford, R., and Rusaw, R. (2015). Development Of Asset Fault Signatures For Prognostic And Health Management in the Nuclear Industry, Prognostics and Health Management.","DOI":"10.1109\/ICPHM.2014.7036366"},{"key":"ref_18","first-page":"222","article-title":"Modeling approaches for prognostics and health management of electronics","volume":"6","author":"Kumar","year":"2010","journal-title":"Int. J. Perform. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Carnero, M.C., and G\u00f3mez, A. (2016). A multicriteria decision making approach applied to improving maintenance policies in healthcare organizations. Bmc Me. Inf. Decis. Mak., 16.","DOI":"10.1186\/s12911-016-0282-7"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.ress.2016.08.009","article-title":"A review on condition-based maintenance optimization models for stochastically deteriorating system","volume":"157","author":"Alaswad","year":"2017","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3351","DOI":"10.1109\/TIM.2012.2205509","article-title":"Failure detection, isolation, and recovery of multifunctional self-validating sensor","volume":"61","author":"Shen","year":"2012","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"045001","DOI":"10.1063\/1.4944976","article-title":"Fault detection, isolation, and diagnosis of status self-validating gas sensor arrays","volume":"87","author":"Chen","year":"2016","journal-title":"Rev. Sci. Instrum."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.chemolab.2015.05.003","article-title":"Grey bootstrap method for data validation and dynamic uncertainty estimation of self-validating multifunctional sensors","volume":"146","author":"Chen","year":"2015","journal-title":"Chemometr. Intell. Lab. Syst."},{"key":"ref_24","first-page":"1740","article-title":"In quantitative measurement of gas component using multisensor array and NPSO-based LS-SVR","volume":"80","author":"Song","year":"2013","journal-title":"Instrum. Meas. Technol. Conf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1597","DOI":"10.3923\/itj.2012.1597.1604","article-title":"Data-driven health evaluation of multifunctional self-validating sensor using health reliability degree","volume":"11","author":"Shen","year":"2012","journal-title":"Inf. Technol. J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"587","DOI":"10.3390\/s130100587","article-title":"A Novel Health Evaluation Strategy for Multifunctional Self-Validating Sensors","volume":"13","author":"Shen","year":"2013","journal-title":"Sensors"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1016\/j.actaastro.2007.01.071","article-title":"Research on health evaluation system of liquid-propellant rocket engine ground-testing bed based on fuzzy theory","volume":"61","author":"Feng","year":"2007","journal-title":"Acta Astronaut."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.ejor.2012.03.027","article-title":"Dynamic maintenance decision-making for series\u2013parallel manufacturing system based on MAM\u2013MTW methodology","volume":"221","author":"Xia","year":"2012","journal-title":"Eur. J. Oper. Res."},{"key":"ref_29","first-page":"135","article-title":"Maintenance decision making based on different types of data fusion","volume":"14","author":"Berges","year":"2012","journal-title":"Eksploat. i Niezawodn.-Maint. Reliab."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1080\/02796015.2003.12086223","article-title":"Instructional effectiveness and instructional efficiency as considerations for data-based decision making: An evaluation of interspersing procedures","volume":"32","author":"Cates","year":"2003","journal-title":"Sch. Psychol. Rev."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1080\/15598608.2009.10411926","article-title":"Reliability-based decision making: A comparison of statistical approaches","volume":"3","author":"Aughenbaugh","year":"2009","journal-title":"J. Stat. Theory Pract."},{"key":"ref_32","first-page":"672","article-title":"Reliability-centered intelligent maintenance decision-making model","volume":"38","author":"Liu","year":"2012","journal-title":"J. Beijing Univ. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, A., Jiang, J., and Zhang, H. (2014, January 18\u201320). Multi-sensor Image Decision Level Fusion Detection Algorithm Based on D-S Evidence Theory. Proceedings of the Fourth International Conference on Instrumentation and Measurement, Computer, Communication and Control, Harbin, China.","DOI":"10.1109\/IMCCC.2014.132"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1002\/etep.584","article-title":"Classification of power quality disturbances using quantum neural network and DS evidence fusion","volume":"22","author":"He","year":"2013","journal-title":"Eur. Trans. Electr. Power"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1109\/TSG.2015.2388778","article-title":"Research on Multiobjective Group Decision-Making in Condition-Based Maintenance for Transmission and Transformation Equipment Based on D-S Evidence Theory","volume":"6","author":"Wang","year":"2015","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_36","first-page":"1","article-title":"The strategy research on electrical equipment condition-based maintenance based on cloud model and grey D-S evidence theory","volume":"3","author":"Lin","year":"2018","journal-title":"Intell. Decis. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s10845-013-0787-1","article-title":"Condition based maintenance-systems integration and intelligence using Bayesian classification and sensor fusion","volume":"26","author":"Mehta","year":"2015","journal-title":"J. Intell. Manuf."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1097\/ACM.0b013e318212eb00","article-title":"Bayes\u2019 theorem and the physical examination: Probability assessment and diagnostic decision making","volume":"86","author":"Herrle","year":"2011","journal-title":"Acad. Med. J. Assoc. Am. Med. Coll."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1244","DOI":"10.1049\/iet-gtd.2013.0124","article-title":"Intelligent maintenance model for condition assessment of circuit breakers using fuzzy set theory and evidential reasoning","volume":"8","author":"Lin","year":"2014","journal-title":"IET Gener. Transm. Distrib."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yin, K., Yang, B., and Li, X. (2018). Multiple Attribute Group Decision-Making Methods Based on Trapezoidal Fuzzy Two-Dimensional Linguistic Partitioned Bonferroni Mean Aggregation Operators. Int. J. Environ. Res. Public Health, 15.","DOI":"10.3390\/ijerph15020194"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.inffus.2015.03.002","article-title":"Fuzzy multiattribute group decision making based on intuitionistic fuzzy sets and evidential reasoning methodology","volume":"27","author":"Chen","year":"2016","journal-title":"Inf. Fusion"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.asoc.2015.09.037","article-title":"An integrated fuzzy multi criteria group decision making approach for ERP system selection","volume":"38","author":"Efe","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.ejor.2015.06.047","article-title":"Interval-valued intuitionistic hesitant fuzzy Choquet integral based TOPSIS method for multi-criteria group decision making","volume":"248","author":"Joshi","year":"2016","journal-title":"Eur. J. Oper. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1007\/s00521-015-1891-2","article-title":"TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment","volume":"27","author":"Pramanik","year":"2016","journal-title":"Neural Comput. Appl."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1109\/TSG.2015.2388778","article-title":"Research on multiobjective group decision-making in condition-based maintenance for transmission and transformation equipment based on D-S evidence theory","volume":"6","author":"Wang","year":"2015","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_46","first-page":"2342","article-title":"Simplified neutrosophic sets and their applications in multi-criteria group decision-making problems","volume":"47","author":"Peng","year":"2016","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1016\/j.ins.2017.11.059","article-title":"Multiattribute group decision making based on intuitionistic 2-tuple linguistic information","volume":"430","author":"Liu","year":"2018","journal-title":"Inf. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"17663","DOI":"10.1016\/j.ijhydene.2012.08.137","article-title":"A grey-based group decision-making methodology for the selection of hydrogen technologies in life cycle sustainability perspective","volume":"37","author":"Manzardo","year":"2012","journal-title":"Int. J. Hydrog. Energy"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1007\/s00521-014-1814-7","article-title":"The multi-attribute group decision-making method based on the interval grey uncertain linguistic generalized hybrid averaging operator","volume":"26","author":"Liu","year":"2015","journal-title":"Neural Comput. Appl."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/7\/2316\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:12:37Z","timestamp":1760195557000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/7\/2316"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,17]]},"references-count":49,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2018,7]]}},"alternative-id":["s18072316"],"URL":"https:\/\/doi.org\/10.3390\/s18072316","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7,17]]}}}