SBIR-STTR Award

This research effort focuses on the development and implementation of methods to capture and analyze electrical current flow deviations in units under test (UUT). The research evaluates technologies and methods to capture electrical current flow. The results of this research are intended to augment the current automatic test equipment measuring capabilities. Electrical current signatures are captured using power supply current sensors and magnetic field sensors. The measurements are then stored in a knowledge database. Software statistical processing identifies critical conditions and issues advisories. The software analyzes steady-state or transient inputs from the knowledge database. TQS will pursue three different software tools: statistical process monitoring, correlation, and neural networks. Software will be used to detect conditions, patterns and trends that signal critical conditions. Adding current measurements increases the ability to troubleshoot and forecast component failure in UUT. Adding current measurements will help correlate electrical current flow deviations to specific components. This added capability should reduce repair times and cost as well as greatly increase the ability of new technicians. A method of characterizing circuits has many commercial applications for any company that repairs or produces electronic devices.The technology anticipated as a result of this research and development should significantly improve decision support for sustaining engineers and technicians. Adding electrical current flow characteristics to emerging ATE should provide increased capability to detect degrading circuit performance and thereby improved failure forecasting capability. Commercial applications of failure forecasting or prognostics are boundless and critical in the aerospace, automotive and medical industries

Automatic test equipment typically uses voltage measurements to identify failures in electronic equipment. In practice electrical current (EC) measurements are very seldom performed due to the intrusive aspect of the EC measurement. This Phase II effort provides a significant opportunity to develop a process to capture EC measurements as well as methods to extract and display steady state and transient EC information. Steady state information indicates the static performance of a circuit. Analysis of the transient portion of the EC measurement introduces new valuable information regarding the reactive or dynamic performance of the circuit. Reactive components can then be diagnosed by their dynamic performance. The capability of simultaneously measuring the complex voltage and EC provides new and valuable insight into the impedance. This newly available information will be input to Neural Networks for both training and analysis of failure ambiguities. The prototype will introduce innovative capabilities for test equipment technicians, and test software engineers. This effort will demonstrate the use of EC data and information to support fault diagnostic procedures in Ogden depot for the F-16 and other weapon systems. It should be extendable to other military and commercial aeronautical, automotive, medical, and industrial applications