This asset management advance designed to help engineers monitor and report the health status of machines, structures, components and systems, was done in collaboration with the National Science Foundation’s Industry/University Cooperative Research Center on Intelligent Maintenance Systems at the University of Cincinnati.
“Companies are looking for a systematic approach to rapidly develop and deploy prognostics for failure prevention, health monitoring and machinery prognostics,” said Dr. Jay Lee, professor and director of the Center on Intelligent Maintenance Systems.
The Watchdog Agent Prognostics Toolkit for NI LabVIEW (www.ni.com/watchdogagent) gives engineers a ready-to-run prognostics solution that can greatly increase engineering efficiency for developing any PHM application, said Lee. The toolkit works with the advanced signal processing capabilities of LabVIEW and the extensive analysis of the NI Sound and Vibration Measurement Suite.
“It provides a set of algorithms including logistic regression, statistical pattern matching, a self-organizing map, a support vector machine and a Gaussian mixture model. Engineers can use these algorithms to create machine and component status descriptors of operating states and failure modes.,” Lee explained. The algorithms convert multiple field sensory readings into summarized health information values for efficient monitoring. The toolkit also includes a health radar chart that displays organized health values of multiple machine components on a single display.
The Watchdog Agent toolkit also can read history data collected from the NI measurement hardware including NI CompactRIO, CompactDAQ, PXI and PCI, and it integrates with the IOtech eZ-TOMAS Technical Data Management Streaming (TDMS) data plug-in, vibDaq from CalBay Systems or any other sensory data acquisition systems based on NI TDMS data files.
