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4 ways predictive analytics can improve equipment maintenance

Posted by Bala Deshpande on Thu, Dec 19, 2013 @ 09:39 AM

Maintenance is generally classified into three classes: corrective maintenance (CM), preventive maintenance (PM), condition based maintenance (CBM) which more recently has been termed predictive maintenance (PdM). 

Condition-based maintenance is maintenance when the need arises. This maintenance is performed after one or more indicators show that equipment is going to fail or that equipment performance is deteriorating. Condition-based maintenance was introduced to try to maintain the equipment at the right time. CBM is based on using real-time data to prioritize and optimize maintenance resources. Observing the state of the system is known as condition monitoring. Such a process will determine the equipment's health, and act only when maintenance is actually necessary. Developments in recent years have allowed extensivepredictive maintenance to manage equipment degradation resized 600
instrumentation of equipment, and together with better tools for analyzing condition data, the maintenance personnel will be able to decide when is the right time to perform maintenance on some piece of equipment. Ideally condition-based maintenance will allow the maintenance personnel to do only the needed things, minimizing spare parts cost, system downtime and time spent on maintenance.

Telematics enabled sensor data is used to understand equipment degradation process or predict failure based on past failure history or pattern or circumstances in which failure took place. Different data mining methods can be used to detect varieties of failure modes. Some of the tools are briefly described below.

  1. Trend Analysis: Reviews the data to find if the asset being monitored is on an obvious and immediate downward slide toward failure. Typically a minimum of three monitoring points are recommended for arriving at a trend accurately as a reliable measure to find if the condition is deprecating linearly.
  2. Pattern recognition: Decodes the causal relations between certain type of events and machine failures. For example, after being used for a certain product run, one of the components used in the asset fails due to stresses that are unique to that run
  3. Critical range and limits: Tests to verify if the data is within a critical range limit (set by professional experience). However machine learning schemes can be adopted to eliminate user intuition for setting these limits.
  4. Statistical process analysis: Existing failure record data (retrieved from warranty claims, data archives and case-study histories) is driven through analytical procedures to find an accurate model for the failure curves and the new data is compared against those models to identify any potential failures.

New mechanisms for analyzing sensors data include configuring big data platforms for data collection, management and analytics and are making inroads into sensor data analytics for CBM. Powered by machine learning and advanced analytics, this can generate several benefits. Here are a few advantages of CBM in no particular order.

  • Detecting failures in early stages and preventing them
  • Finding ‘Remaining life of asset’
  • Schedule predictive maintenance
  • Maintaining “Right level of inventory” for spare parts
  • Evaluate “What if” alternate scenarios
  • Determine right warranty period for the assets at the design time
  • Predict breakdown
  • Notify operator at right time
  • Prevent risk of collateral damage and secondary failure
  • Prevent high production downtime
  • Maximizing equipment life

Join us at the inaugural 2014 Predictive Analytics World-Manufacturing conference to hear more about predictive maintenance and failure detection.

Predictive analytics world manufacturing


Topics: manufacturing analytics, machine data analytics, big data

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