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Development of a utility function for sensor networks from a fault diagnosis perspective
Dr. N. Sridhar (Sridharakumar Narasimhan), Norwegian University of Science and Technology, Norway
Date & Time: June 23, 2006 11:00
Venue: Electrical Engineering Seminar Hall
Abstract:

Safety and optimality are crucial requirements in every industrial process and the importance of a reliable fault diagostic technique cannot be over-emphasised. The success of any fault diagnosis technique depends critically on the sensors measuring the important process variables. Choosing an appropriate sensor network is a combinatorially difficult problem, especially when the number of potential measurements is large. There has been considerable amount of work that has been done on developing algorithms for sensor network design for fault diagnosis based on quantitative and qualitative models. Various objectives such as cost, reliability and fault resolution have been used in the sensor network design. While these design algorithms can provide the best design locations for a given cost, the value of the sensor network for fault diagnosis or benefit accrued is usually not quantified in a manner that is transparent to the user. This is an important aspect that needs to be addressed if these algorithms have to be assimilated into industrial practice. In this work, an approach for characterizing the value of a sensor network from a fault diagnosis perspective is proposed. This notion of value can be used directly in sensor network design algorithms.

A two step procedure for determining the value of a given sensor network from a fault diagnosis perspective is described in this contribution. In the first step, the set of resolvable faults for the given sensor network is determined. Resolvability refers to the property of the sensor network to identify the exact fault that has occurred based on available symptoms. This is a function of the chosen sensor network, the mode of occurrence of the faults and the fault diagnosis strategy employed. Subsequently, the value of the sensor network is determined using a probabilistic approach that captures the underlying process economics. The proposed concepts are explained through numerical examples.

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