TIdata - Predictive Maintenance  
   
 

 
Statistical Analysis/Predictive Analytics

Predictive Analytics

Predictive Analytics is an advanced form of statistical analysis that combines data from multiple technologies: operational data, equipment sensor data, lubrication data and other data inputs. Predictive Analytics uses trending and variant analysis coupled with multiple regression analysis to predict when a failure will occur or the rate of degradation of a component. Using Paired or Multiple Indicators to build predictive models increases the confidence level of the results.

The PdM System enables intimate understanding of the health of the machinery at all times. It enables users to reduce costs and improve efficiency of the overall maintenance management process by planning work based on the actual maintenance need rather than a time based prescription. It provides relevant data to trend and analyze. With the data, predictive analytic modeling can be applied to create an effective rule structure with early warning of potential failures. The PdM System is designed to avoid missed detections and minimize false alarms.

Most failures involve not just one fault but also a series of different faults developed over time. The PdM System is designed to discover and correlate the development of multiple faults early enough to plan remedies and avoid unplanned failures.
 
 
   
 
 
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