Reliable and Diagnosis-based Sensor Network, Design and Retrofitting
Systematic methods and tools for managing the complexity
Process Operation, Monitoring & Analysis (T4-2)
Keywords: Sensor network, fault diagnosis, design & retrofit
The optimal design and upgrade of sensor networks (SN) has been receiving an increased attention for the last few years. A wide-ranging variety of approaches relying on exhaustive enumeration, algorithmic procedures, rigorous mathematical models and meta-heuristic techniques were developed to address the complexity of the sensor network placement problem, usually assuming a steady-state system at the nominal operating conditions. Within this context, the sensor placement can be regarded as a highly combinatorial optimization problem where the main goal is to find the optimal balance between the performance indicators and the cost of the data acquisition system [1, 2].
On the other hand, in most of the articles dealing with fault diagnosis it is assumed that the SN is already in place and the relationship between sensor location and the Fault Diagnosis System (FDS) performance is rarely discussed. In this work, both issues are considered and a methodology to optimally design a SN which on-line supplies reliable data to a FDS is presented. In that sense, sensors are placed to minimize the sum of investment costs while keeping the original FDS performance when all measured variables were originally considered.
The way in which the investment cost is minimized, is based on the selection of just some process key variables that allows maintaining the FDS performance. These variables will gather the essential information given at each moment by the process from the fault diagnosis point of view. Then a methodology, based on an MINLP formulation, that assures the observability of the process as well as the minimum -imposed as input- of the reliability of such process key variables estimation is applied in order to allocate the required instrumentation. In order to check the methodology, a data based FDS and a challenging diagnosis problem such as the Tennessee Eastman benchmark [3] is considered. The FDS is based on a PCA detection module integrated with a rules based fuzzy logic system that on-line interpret the statistics calculated from PCA [4]. In order to estimate its performance, the accuracy, defined as the rate of right diagnosis and the total diagnosis responses, is evaluated.
The formulated sensor placement optimization problem is solved using an MINLP-based approach [5, 6]. Three cases have been taken into account: design without the knowledge of the FDS key variables, design with the knowledge of FDS key variables, and retrofit, with the knowledge of the key variables, of the sensor network solution obtained without it.
The results obtained show how the knowledge of the key variables in the design phase, make it possible accomplishing with the reliability requirements for them with less investment than that needed during the retrofitting phase.
Acknowledgments
Financial support received from the European Commission (PRISM project MRTN-CT-2004-512233) is fully appreciated.
References
[1] Ali, Y. & Narasimahan, S. “Sensor network design for maximizing reliability of linear processes”, AIChe Journal, 39, pp. 820-826 (1993).
[2] Madron, F. & Veverka, V. “Optimal selection of measuring points in complex plants by linear models”, AIChE Journal, 38, pp. 227-236 (1992).
[3] Downs, J.J and Vogel, E.F. “A plant-wide industrial process control problem”, Comput. Chem. Eng., 17, pp. 245-255 (1993).
[4] Musulin, E., Yélamos, I., Puigjaner, L. “Integration of Principal Component Analysis and Fuzzy Logic System for comprehensive fault diagnosis”, ”, Ind. Eng. Chem., Res, 45, 1739-1750.
[5] Angelini, R., Méndez, C.A., Musulin E., Puigjaner, L. “An optimization framework to computer-aided design and upgrade of measurement systems”, To be presented at ESCAPE 16 – PSE 2006, Paper 1464, Garmisch-partenkirchen, Germany, July 9 – 13 (2006)
[6] Benquilou, C., Graells, M., Musulin, E., Puigjaner, L. “Design and Retrofit of Reliable Sensor Networks”, Ind. Eng. Chem., Res, 43, pp. 8026-8036 (2004).
Presented Tuesday 18, 09:45 to 10:05, in session Process Operation, Monitoring & Analysis (T4-2).
