Modern engineering systems are beginning to operate in data-rich environments. Sensor technologies are becoming available to interrogate any aspect of modern engineering systems at every conceivable scale of resolution. For example, many manufacturing machine operations, consumer products (at an item or a package level), infrastructures like bridges, pipelines and railroads, and combatant ships like DD(X) are starting to be fitted with RF and other sensory devices. These sensors can capture wealth of information on the condition and status of a good, machine or a system. The next major challenge is in harnessing the large-amounts of sensor data to bring substantial improvements to the design and operations, particular in quality and integrity assurance, of these engineering systems, which include many precision manufacturing machines and processes, the Internet, supply networks and infrastructure and lifelines systems.

Complexity is a chief attribute of modern engineering systems. Much of the complexity emerges from nonlinear stochastic dynamics of the underlying processes. In fact, dynamics of most manufacturing machine operations as well as large-scale infrastructures, logistic and information networks, are inherently nonlinear. New modeling foundations that can capture this complex dynamics are imperative for effective quality and integrity monitoring.

Sensor-based modeling research provides a unique approach to realize this imperative. It augments the statistical and intelligent systems foundations of current monitoring systems with nonlinear dynamic principles. Features extracted from these models, unlike conventional features, are sensitive to incipient anomalies and micro dynamic variations in a system’s operation. Consequently, yield and integrity can be significantly improved, and wastage and accidents can be substantially reduced. The research will ultimately contribute to a new paradigm where an engineering system is autonomized at all scales. The specific research objectives are to:

• study the origins of complicated patterns in sensor signals from manufacturing machines, processes, and specific infrastructure and lifeline systems,

• derive theory and methods to capture the dynamics underlying these signals for quality and integrity monitoring.