NSF - Health Monitoring

Equipment supervisors are faced with the constant concern of trying to strike
a balance between the functionality of their machinery and the costs
associated with maintenance and downtime. The limitations inherent in this
human decision-making process result in an inability to strike an optimal
balance. In the past, technology has not been very helpful in analyzing
information to beget this optimal balance. However, new advances in sensor
technology, failure analysis techniques, system predictive modeling, data
fusion and automated reasoning algorithms could make it possible for these
technologies to provide such a capability.

The focus of this NSF (National Science Foundation) Phase II project is on enhancing maintenance operations scheduling methodologies with condition assessment and diagnostic tools to produce an "integrated" maintenance management system.  TSi has already developed scheduling tools that allocate resources on the basis of elapsed calendar time and unit utilization.

The goal here is the augmentation of our existing scheduling tools with
condition assessment modules to produce a generally applicable system
combining condition, time, and use as drivers for the maintenance process.
This involves the development of innovative condition assessment techniques
for industrial machinery and the incorporation of those techniques into
health monitoring systems for predictive maintenance.