Using Data Science For Predictive Maintenance

Sandeep Raut

A few years ago, there were two recall announcements from the National Highway Traffic Safety Administration, warning of problems that could cause fires in two auto brands. For both automakers, these defect required significant money and time to resolve.

Manufacturers in the aerospace, rail, equipment, and automotive industries face the challenge of ensuring maximum availability of critical assembly line systems and keeping those assets in good working order, while simultaneously minimizing the cost of maintenance and time-based or count-based repairs. Identifying root causes of faults and failures must also happen without labs or testing. As more vehicles, industrial equipment, and assembly robots communicate their status to a central server, detection of faults becomes easier and more practical.

Identifying potential issues early helps organizations deploy maintenance teams more cost-effectively and maximizes parts and equipment uptime. All the critical factors that help predict failure may be deeply buried in structured data (including equipment year, make, model, and warranty details) and unstructured data comprising millions of log entries that include sensor data, error messages, odometer readings, speeds, engine temperatures, engine torque and acceleration records, and repair and maintenance reports.

Predictive maintenance, a technique for predicting 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. For example, TrenItalia has invested 50 million euros in an Internet of Things project to cut maintenance costs by up to 130 million euros and increase train availability and customer satisfaction.

The benefits of using data science with predictive maintenance include:

  • Minimized maintenance costs. Don’t waste money through over-cautious, time-bound maintenance. Repair equipment only when repairs are actually needed.
  • Reduced unplanned downtime. Implement predictive maintenance to predict future equipment malfunctions and failures, and minimize the risk for unplanned disasters that could put 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. Stop wasting time replacing and fixing equipment that doesn’t need it.
  • Avoidance of warranty cost to recover failure. Minimize recalls and assembly-line production loss.

Sudden machine failures can result in contract penalties and lost revenue, and can even ruin the reputation of a business. Data science can help avoid problems in real time and before they happen.

For more on how predictive analytics can improve business efficiency, see Using Algorithms To Add Science To Human Judgement In HR.

This article originally appeared in Simplified Analytics.