The performance evaluation of sensors is very important in actual application. In this paper, a theory based on multi-variable information fusion is studied to evaluate the health level of multifunctional sensors. A novel conception of health reliability degree ( HRD) is defined to indicate a quantitative health level, which is different from traditional so-called qualitative fault diagnosis. To evaluate the health condition from both local and global perspectives, the HRD of a single sensitive component at multiple time points and the overall multifunctional sensor at a single time point are defined, respectively. The HRD methodology is emphasized by using multi-variable data fusion technology coupled with a grey comprehensive evaluation method. In this method, to acquire the distinct importance of each sensitive unit and the sensitivity of different time points, the information entropy and analytic？hierarchy process method are used, respectively. In order to verify the feasibility of the proposed strategy, a health evaluating experimental system for multifunctional self-validating sensors was designed. The five different health level situations have been discussed. Successful results show that the proposed method is feasible, the HRD could be used to quantitatively indicate the health level and it does have a fast response to the performance changes of multifunctional sensors.
References
[1]
Kimoto, A.; Shida, K. A new multifunctional sensor using piezoelectric ceramic transducers for simultaneous measurements of propagation time and electrical conductance. IEEE Trans. Instrum. Meas. 2008, 57, 2542–2547.
Wei, G.; Shida, K. Estimation of concentrations of ternary solution with NaCl and sucrose based on multifunctional sensing technique. IEEE Trans. Instrum. Meas. 2006, 55, 675–681.
[4]
Shen, Z.G.; Zhu, F.Y.; Wang, Q. Research on state self-validation of multifunctional sensor based on PFP-WRVM. Chin. J. Sci. Instrum. 2012, 9, 1986–1994.
[5]
Wang, Q.; Shen, Z.G.; Zhu, F.Y. Failure Detection and Validation of Multifunctional self-Validating Sensor Using WRVM Predictor. Proceedings of the IEEE International Conference on Industrial Technology (ICIT), Kos Island, Greece, 19–21 March 2012; pp. 343–348.
[6]
Feng, Z.G.; Wang, Q.; Shida, K. Design and implementation of a self-validating pressure sensor. IEEE Sens. J. 2009, 9, 207–218.
[7]
Feng, Z.G.; Wang, Q.; Shida, K. A review of self-validating sensor technology. Sens. Rev. 2007, 27, 48–56.
[8]
Shen, Z.G.; Wang, Q.; Zhu, F.Y. Data-driven health evaluation of multifunctional self-validating sensor using health reliability degree. Infor. Tech. J. 2012, 11, 1597–1604.
[9]
Shen, Z.G.; Wang, Q. Failure detection, isolation and recovery of multifunctional self-validating sensor. IEEE Trans. Instrum. Meas. 2012, 61, 3351–3362.
[10]
Feng, Z.G.; Wang, Q. Research on health evaluation system of liquid-propellant rocket engine ground-testing bed based on fuzzy theory. Acta Astronaut. 2007, 61, 840–853.
[11]
Zhu, D.Q.; Bai, J.; Yang, S.X. A multi-fault diagnosis method for sensor systems based on principle component analysis. Sensors 2010, 10, 241–253.
[12]
Martínez-Sibaja, A.; Astorga-Zaragoza, C.M.; Alvarado-Lassman, A. Simplified interval observer scheme: A new approach for fault diagnosis in instruments. Sensors 2011, 11, 612–622.
[13]
Heredia, G.; Ollero, A. Virtual sensor for failure detection, identification and recovery in the transition phase of a morphing aircraft. Sensors 2011, 10, 2188–2201.
[14]
Li, L.P.; Zou, X.Y.; Jin, F.H. The method based on fusion information entropy for quantitative assessing vibration state in large capacity rotary machinery. J. Power Eng. 2004, 24, 153–158.
[15]
Cao, Z.H.; Shen, J.H. Sensor health degree evaluation method based on fuzzy set theory. J. Electr. Mach. Control 2010, 14, 79–85.
[16]
Elouedi, Z.; Mellouli, K.; Smets, P. Assessing sensor reliability for multi-sensor data fusion within the transferable belief model. IEEE Trans. Syst. Man Cybern. Part B Cybern. 2004, 34, 782–787.
[17]
Huang, K.Y. An auto-recognizing system for dice games using a modified unsupervised grey clustering algorithm. Sensors 2008, 8, 1212–1221.
[18]
Tunga, C.T.; Lee, Y.J. The innovative performance evaluation model of grey factor analysis: A case study of listed biotechnology corporations in Taiwan. Expert Syst. Appl. 2010, 37, 7844–7851.
[19]
Büyük？zkan, G.; ？if？i, G.; Güleryüz, S. Strategic analysis of healthcare service quality using fuzzy AHP methodology. Expert Syst. Appl. 2011, 38, 9407–9424.
[20]
Pedrycz, W.; Song, M. Analytic hierarchy process in group decision making and its optimization with an allocation of information granularity. IEEE Trans. Fuzzy Syst. 2011, 19, 527–539.
[21]
Hurley, W.J. The analytic hierarchy process: A note on an approach to sensitivity which preserves rank order. Comput. Oper. Res. 2001, 28, 185–188.
[22]
Ramanathan, R. A note on the use of the analytic hierarchy process for environmental impact. J. Environ. Manag. 2001, 63, 27–35.
[23]
Yi, X.Q.; Zhu, X.T. Application of Entropy Weight Coefficient Method to the Evaluation of the Profitability of Listed Firms. Int. Conf. Comput. Appl. Syst. Model. 2010, 5, 108–110.
