Introduction:

Equipment maintenance cost billions of dollars annually and equipment down time is a major impediment to improving productivity.  The Center for Systems Reliability (CSR) is developing new tools and methodologies to improve maintenance strategies and decrease equipment down time. 

Predictive Maintenance
Predictive maintenance can be defined as the ability to estimate the likelihood of an equipment failure over some future time interval so that problems can be identified and maintenance performed before the failure occurs.  CSR's approach to providing a predictive capability is to combine real-time data from sensors imbedded in the equipment with a  Windows-based reliability analysis tool, WinR-PdM™.


Spares Optimization
Maintaining an adequate spares inventory, while crucial for minimizing equipment down time, can also be very expensive.  CSR's WinR™ can be used to develop an optimal spares inventory.  By "optimal"  we mean an inventory that results in the greatest reduction in equipment down time for the lowest cost.

 
Maintenance Cost Analysis
While equipment maintenance is a major cost factor for most production operations, it is seldom quantified.  More importantly, few companies take the time to identify the key contributors to maintenance cost.  CSR's WinR-DP™ software tool can help quantify equipment maintenance cost as well as identifying the top contributors to total cost.


Failure Data Analysis
There are several metrics by which we determine equipment performance such as mean time between failures (MTBF), availability (the probability the equipment is available for use when needed), down time (the time that equipment should be operating but is not), or cost.   Improving equipment performance may involve improving any or all of these metrics.   CSR's WinR-DP™ software can be used to quantify these metrics and determine key contributors to all of them.