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Predictive Maintenance

Predictive Maintenance

A Predictive Maintenance platform for the IoT Era

Predictive Maintenance platforms (PMP) should offer a few key features.  First, they should give operations the ability to constantly and frequently up monitor the health of all of their critical assets.  This means ops should know the likelihood of all possible failures and part wear-outs well in advance such that preventative maintenance can be scheduled to maximize up-time and extend asset life.

With the massive investments made in instrumentation (IoT) over the past few years a PMP should also fully leverage these investments by consuming and extracting value from sensor data and unstructured reports, logs, etc.

The challenge with this “big data” is that it has a very low information density and is not readily usable by either humans or machine learning.  Furthermore, predicting failures using machine learning/AI is just part of an overall approach to optimizing maintenance schedules. Fixed and variable costs must also be taken into account.

Medea is the first platform that fully addresses all of these challenges and delivers truly optimized maintenance schedules to maximize up-time and asset life.

QueBIT’s Medea Predictive Maintenance solution fully utilizes the rich data, that was previously locked-up, generated by telematic sensors.  Marrying this data with master data, Medea leverages sophisticated machine learning algorithms (and advanced AI) to build optimized maintenance schedules that maximize the financial benefit of predictive maintenance.

Benefits include:

  • Fully automated analysis of massive amounts of telematic sensor data
  • Near real-time fleet/asset health monitoring
  • Optimized maintenance schedules based on financial benefit
  • Extended asset life
  • Increased uptime/production

Medea fully leverages the power of Hadoop and Spark to analyze your big data!

medea_health_monitoring
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Medea allows you to monitor the health of all your assets in near real-time using a number of different visualizations (treemap, map, etc.). This allows asset managers to identify assets that need maintenance and assets that can wait for maintenance.
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Medea analyzes sensor (telematic) data; making it useful for predictive modeling. Petabytes of data can be consumed and converted into key metrics that allow predictive models to predict the probability of future failures.

Download Medea Predictive Maintenance Brochure now!

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