The research objective of this Sensors and Sensor Networks (NSF 05-526) project is to acquire fundamental understanding and establish a reliability modeling, analysis and optimization methodology for sensor systems used for variation source identification in manufacturing processes. The approach is threefold. First, the reliability structure of sensor systems will be studied based on system reliability theory and identifiability analysis. Second, efficient reliability analysis algorithms will be generated through investigation of analogues between sensor system reliability and network reliability models. Last, a method using penalty functions and exchange algorithms, which have been mostly used for optimum experimental design, will be applied for sensor placement design and reliability optimization. The models and algorithms will be verified and implemented in printed circuit assembly processes.
The success of this project will contribute significantly to the science base of system reliability modeling, analysis, and design for sensor systems used for variation source identification. Variation source identification using systems of sensors and sensor arrays is essential for manufacturing quality and productivity improvement, as well as signal source detection in various sensor array signal-processing applications. Sensor system failure in these applications may result in the loss of critical information, leading to numerous undesirable outcomes ranging from inferior manufacturing quality to catastrophic failure and even loss of human life in navigation, space, and defense applications. The methodology established in this research will enable designers to assure sensor system reliability by systematically incorporating system reliability information in the design phase. Design of reliable sensor systems for variation source identification will reduce manufacturing downtime, improve product quality, and in turn generate substantial economic impact on manufacturing industries.
