Using Data Science for Predictive Maintenance

Using data science for predictive maintenance

Remember a few years ago there were two recall announcements from the National Highway Traffic Safety Administration for GM & Tesla – both related to problems that could cause fires? These cost literally tonnes of money to resolve.

Aerospace, Rail industries, Equipment manufacturers and Auto-makers often face this challenge of ensuring maximum availability of critical assembly line systems, keeping those assets in good working order, while simultaneously minimizing the cost of maintenance and time-based or count-based repairs.

Identification of root causes of faults and failures must also happen without the need for a lab or testing.  As more vehicles/industrial equipment and assembly robots begin to communicate their current status to a central server, detection of faults becomes more easy and practical.

Early identification of these potential issues helps organizations deploy maintenance teams more cost effectively and therefore maximize parts/equipment up-time.  All the critical factors that help to predict failure, may be deeply buried in structured data-like equipment; year, make, model, warranty details etc and unstructured data covering millions of log entries, sensor data, error messages, odometer readings, speed, engine temperatures, engine torque, acceleration and repair & maintenance reports.

Predictive maintenance, a technique to predict when an in-service machine will fail so that maintenance can be planned in advance encompasses failure prediction, failure diagnosis, failure type classification, and recommendation of maintenance actions after failure.

Business benefits of Data Science with predictive maintenance:

  • Minimize maintenance costs – Don’t waste money through over-cautious time bound maintenance. Only repair equipment when repairs are actually needed.
  • Reduce unplanned downtime – Implement predictive maintenance to predict future equipment malfunctioning and failures and minimize the risk for unplanned disasters putting your business at risk.
  • Root cause analysis – Find causes for equipment malfunctions and work with suppliers to switch-off reasons for high failure rates. Increase return on your assets.
  • Efficient labor planning – no time wasted replacing/fixing equipment that doesn’t need it
  • Avoid warranty cost for failure recovery – thousands of recalls in case of automakers while production loss in assembly line

TrainItalia has invested 50M euros in their Internet of Things project which expects to cut maintenance costs by up to 130M euros to increase train availability and customer satisfaction.

Rolls Royce is teaming up with Microsoft for Azure based streaming for predicting engine failures and ensuring the correct maintenance steps.

In conclusion: sudden machine failures can ruin the reputation of a business resulting in potential contract penalties, and lost revenue.  Data Science can help in real time and thereby save this trouble.

Read more by Sandeep Raut, here

 

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