Abstract
Hospitals’ daily operations have become increasingly dependent on medical devices. However, the occurrence of faults is inevitable. Therefore, it is crucial for hospitals to make timely fault diagnoses and enact the corresponding measures and improvements. This paper proposes a novel concept lattice method for the intelligent diagnosis of medical device faults. To minimize the influence of uncertain factors, fuzzy sets are used to accurately express relationships between concepts. First, the occurrence frequency and severity of each fault type are extracted based on the collected information. Then, the fuzzy formal context of occurrent faults and known faults can be constructed. Next, the corresponding fuzzy concept lattice is established and visualized using a Hasse diagram. Finally, the similarity between the concept lattices is calculated and used for fault diagnosis. Here, the weight factors are determined using the decision-making trial and evaluation laboratory (DEMATEL) method. A comparative analysis is performed to show that the proposed method uses simple calculations and is highly accurate.






Similar content being viewed by others
Explore related subjects
Discover the latest articles and news from researchers in related subjects, suggested using machine learning.References
Wang, Y., Kung, L., Byrd, T.A.: Big data analytics: understanding its capabilities and potential benefits for healthcare organizations. Technol. Forecast. Soc. Chang. 126, 3–13 (2018)
Agha, R.A., Fowler, A.J., Saeta, A., Barai, I., Rajmohan, S., Orgill, D.P., Aronson, J., et al.: The SCARE statement: consensus-based surgical case report guidelines. Int. J. Surg. 34, 180–186 (2016)
Tiryakioglu, B., Kayakutlu, G., Duzdar, I. Medical device tracking via QR code and efficiency analyze. Portland International Conference on Management of Engineering and Technology. IEEE, 2016, 3115-3128
Zeng, X.N., Shao, L., Xue, H.: Study on the fast locating technology of telemedicine device fault. China Med. Dev. 32(5), 64–67 (2017)
Wang, X., Wang, J., Privault, M.: Artificial intelligent fault diagnosis system of complex electronic device. J. Intel. Fuzzy Syst. 1, 1–11 (2018)
Zhang, H., Liu, J., Kato, N.: Threshold tuning-based wearable sensor fault detection for reliable medical monitoring using Bayesian network model. IEEE Syst. J. 12(2), 1886–1896 (2018)
Lyons, I., Blandford, A.: Safer healthcare at home: detecting, correcting and learning from incidents involving infusion devices. Appl. Ergon. 67, 104–114 (2018)
Firouzi, F., Rahmani, A.M., Mankodiya, K., Badaroglu, M., Farahani, B.: Internet-of-things and big data for smarter healthcare: from device to architecture, applications and analytics. Fut. Gen. Comput. Syst. 2017(78), 583–586 (2018)
Resnic, F.S., Majithia, A., Marinac-Dabic, D., Robbins, S., Ssemaganda, H., Hewitt, K., Normand, S.L.: Registry-based prospective, active surveillance of medical-device safety. N. Engl. J. Med. 376(6), 526–535 (2017)
Zhang, H., Liu, J., Li, R., Le, H.: Fault diagnosis of body sensor networks using hidden Markov model. Peer-to-Peer Netw. Appl. 10(6), 1285–1298 (2016)
AbdElfattah, E., Elkawkagy, M., El-Sisi, A. A reactive fault tolerance approach for cloud computing. International Computer Engineering Conference (ICENCO), IEEE. 2017, 13, 190-194
Tang, Y., Wang, C., Wang, M., Hao, H., Zhao, J. Based on self-learning dictionary circuit board fault diagnosis device. Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), IEEE. 2017, 2, 2653-2657
Jansen, C., Schollmeyer, G., Augustin, T.: Concepts for decision making under severe uncertainty with partial ordinal and partial cardinal preferences. Int. J. Approx. Reason. 98, 112–131 (2018)
Spruyt, B., van Noll, J., van Bossche, L.: Meaning matters. An empirical analysis into public denotations of the label ‘strangers’ and their relationship with general ethnic prejudice. Int. J. Intercult. Relat. 51, 41–53 (2016)
Mashkoor, A., Biro, M.: Towards the trustworthy development of active medical devices: a hemodialysis case study. IEEE Embed. Syst. Lett. 8(1), 14–17 (2015)
Yang, H.C., See, K.Y., Simanjorang, R., Li, K.R.: Offline health diagnosis of power device based on non-intrusive inductively coupled approach. IEEE Journal of Emerging and Selected Topics in Power Electronics 6(4), 2053–2059 (2018)
Lei, Y., Jia, F., Lin, J., Xing, S., Ding, S.: An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Trans. Industr. Electron. 63(5), 3137–3147 (2016)
Kohani, M., Pecht, M.: Malfunctions of medical devices due to electrostatic occurrences big data analysis of 10 years of the FDA’s reports. IEEE Access 6, 5805–5811 (2018)
Li, W.H., Zhu, C.J.: Research on development trend of data analysis and decision making for hospital facility operation and maintenance. Chin. Hosp. Manag. 38(5), 78–80 (2018)
Li, K.L., Gao, H., Xu, Y.X., Qi, D., Zhang, H., Qian, Y.: Design and application of medical device maintenance management system based on ERP. China Med. Dev. 33(1), 120–122 (2018)
Yang, S.S., Lam, B. H., Ng, C. M. Digital Sampling Technique in the Calibration of Medical Testing Device with Arbitrary Waveforms. IEEE International Symposium on Medical Measurements and Applications (MeMeA), 2018, 1-6
Kaspi, M., Raviv, T., Tzur, M.: Detection of unusable bicycles in bike-sharing systems. Omega 65, 10–16 (2015)
Xiang, J., Zhong, Y.: A novel personalized diagnosis methodology using numerical simulation and an intelligent method to detect faults in a shaft. Appl. Sci. 6(12), 1–19 (2016)
Hazra, A., Das, S., Basu, M.: An efficient fault diagnosis method for PV systems following string current. J. Clean. Prod. 154, 220–232 (2017)
Yang, R., Xiong, R., He, H., Chen, Z.: A fractional-order model-based battery external short circuit fault diagnosis approach for all-climate electric vehicles application. J. Clean. Prod. 187, 950–959 (2018)
Kaid, I.E., Hafaifa, A., Guemana, M., Hadroug, N., Kouzou, A., Mazouz, L.: Photovoltaic system failure diagnosis based on adaptive neuro fuzzy inference approach: South Algeria solar power plant. J. Clean. Prod. 204, 169–182 (2018)
Waseem, A.M., Jonathan, R., Yacine, R.: Predictive modelling for solar thermal energy systems: a comparison of support vector regression, random forest, extra trees and regression trees. J. Clean. Prod. 203, 810–821 (2018)
Yang, Z., Chen, J., Tang, L.T., Wei, X.Q.: System automatic fault diagnosis method based on fuzzy FMEA analysis. Power Syst. Prot. Control 12, 148–153 (2017)
Qiao, Z., Lei, Y., Lin, J., Jia, F.: An adaptive unsaturated bistable stochastic resonance method and its application in mechanical fault diagnosis. Mech. Syst. Sign. Process. 84, 731–746 (2017)
Torkaman, H., Moradi, R., Hajihosseinlu, A., Toulabi, M.S.: A comprehensive power loss evaluation for switched reluctance motor in presence of rotor asymmetry rotation: theory, numerical analysis and experiments. Energy Convers. Manage. 77, 773–783 (2014)
Rehman, H.U., Hirvonen, J., Sirén, K.: Influence of technical failures on the performance of an optimized community-size solar heating system in Nordic conditions. J. Clean. Prod. 175, 624–640 (2018)
Ates, Y., Uzunoglu, M., Karakas, A., Boynuegri, A.R., Nadar, A., Dag, B.: Implementation of adaptive relay coordination in distribution systems including distributed generation. J. Clean. Prod. 112, 2697–2705 (2016)
Wille, R.: Restructuring lattice theory: an approach based on hierarchies of concepts. Orderd Sets D Reidel 83, 314–339 (1982)
Hao, F., Min, G., Pei, Z., Park, D.S., Yang, L.T.: K-clique community detection in social networks based on formal concept analysis. IEEE Syst. J. 11(1), 250–259 (2017)
Singh, P.K., Kumar, C.A., Gani, A.: A comprehensive survey on formal concept analysis, its research trends and applications. Int. J. Appl. Math. Comput. Sci. 26(2), 495–516 (2016)
Fkih, F., Omri, M.N.: IRAFCA: an O(n) information retrieval algorithm based on formal concept analysis. Knowl. Inf. Syst. 48(2), 465–491 (2016)
Jenett, B., Calisch, S., Cellucci, D., Cramer, N., Gershenfeld, N., Swei, S., Cheung, K.C.: Digital morphing wing: active wing shaping concept using composite lattice-based cellular structures. Soft Robot. 4(1), 33–48 (2017)
Sun, X., Liu, X., Li, B., Duan, Y., Yang, H., Hu, J. Exploring topic models in software engineering data analysis: A survey. ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD). IEEE, 2016, 357-362
Valverde-Albacete, F.J., González-Calabozo, J.M., Penas, A., Pelaez-Moreno, C.: Supporting scientific knowledge discovery with extended, generalized Formal Concept Analysis. Expert Syst. Appl. 44, 198–216 (2016)
Singh, P.K., Aswani Kumar, C., Li, J.: Knowledge representation using interval-valued fuzzy formal concept lattice. Soft. Comput. 20(4), 1485–1502 (2016)
De Maio, C., Fenza, G., Loia, V., Orciuoli, F.: Distributed online temporal fuzzy concept analysis for stream processing in smart cities. J. Paral. Distrib. Comput. 110, 31–41 (2017)
Wang, Z., Xu, G., Ren, J., Li, Z., Zhang, B., Ren, X.: Polygeneration system and sustainability: multi-attribute decision-support framework for comprehensive assessment under uncertainties. J. Clean. Prod. 167, 1122–1137 (2017)
Chatterjee, K., Pamucar, D., Zavadskas, E.K.: Evaluating the performance of suppliers based on using the R'AMATEL-MAIRCA method for green supply chain implementation in electronics industry. J. Clean. Prod. 184, 101–129 (2018)
Tian, G., Zhang, H., Feng, Y., Jia, H., Zhang, C., Jiang, Z., et al.: Operation patterns analysis of automotive components remanufacturing industry development in china. J. Clean. Prod. 164, 1363–1375 (2017)
Fontela, E., André, G.: DEMATEL: progress achieved. Futures 6(4), 361–363 (1974)
Wang, Z., Ren, J., Goodsite, M.E., Xu, G.: Waste-to-energy, municipal solid waste treatment, and best available technology: comprehensive evaluation by an interval-valued fuzzy multi-criteria decision-making method. J. Clean. Prod. 172, 887–899 (2018)
Büyüközkan, G., Güleryüz, S., Karpak, B.: A new combined IF-DEMATEL and IF-ANP approach for CRM partner evaluation. Int. J. Prod. Econ. 191, 194–206 (2017)
Zhou, F., Wang, X., Lim, M.K., He, Y., Li, L.: Sustainable recycling partner selection using fuzzy DEMATEL-AEW-FVIKOR: a case study in small-and-medium enterprises (SMEs). J. Clean. Prod. 196, 489–504 (2018)
Si, S.L., You, X.Y., Liu, H.C., Huang, J.: Identifying key performance indicators for holistic hospital management with a modified DEMATEL approach. Int. J. Environ. Res. Public Health 14(8), 1–17 (2017)
Liou, J., Lu, M.T., Hu, S.K., Cheng, C.H., Chuang, Y.C.: A hybrid MCDM model for improving the electronic health record to better serve client needs. Sustainability 9(10), 1–13 (2017)
Shen, X.T., Ye, M.M., Gan, T., Han, D.J., Han, D.J.: Information retrieval based on concept lattice and its tree visualization. Comput. Eng. Appl. 53(3), 95–99 (2017)
Dias, S.M., Vieira, N.J.: A methodology for analysis of concept lattice reduction. Inf. Sci. 396, 202–217 (2017)
Zou, C., Deng, H.: Using fuzzy concept lattice for intelligent disease diagnosis. IEEE Access 5, 236–242 (2017)
Yazdani, M., Chatterjee, P., Zavadskas, E.K., Zolfani, S.H.: Integrated QFD-MCDM framework for green supplier selection. J. Clean. Prod. 142, 3728–3740 (2017)
Xiang, Z.H., Li, Z., Li, J.: Diagnosis model and primary and secondary element analysis of nephritis based on BP neural network. Softw. Guide 15(2), 126–129 (2016)
Acknowledgements
The study was supported by “Shaanxi Natural Science Foundation Project” (2017JM7004), “Fundamental Research Funds for the Central Universities” (JB190606), “The discipline promotion project of the First Affiliated Hospital of Air Force Medical University” (XJZT18MJ49), “Major Theoretical and Practical Research Projects of Social Science in Shaanxi province” (2019C068).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest
The authors declare no conflict of interest.
Rights and permissions
About this article
Cite this article
Guo, X., Liu, A., Li, X. et al. Research on the Intelligent Fault Diagnosis of Medical Devices Based on a DEMATEL-Fuzzy Concept Lattice. Int. J. Fuzzy Syst. 22, 2369–2384 (2020). https://doi.org/10.1007/s40815-020-00859-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40815-020-00859-0