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How predictive maintenance can transform industrial IoT

McKinsey forecasts that the potential economic impact from IoT systems — including consumer surplus — could reach $11.1 trillion per year by 2025. Given the ongoing surge in IoT adoption, it’s no surprise that many industrial companies are rapidly seeking to introduce IoT data into everyday operations. While IoT has already proven itself to be invaluable for its ability to collect data from entirely new sources, it truly shines when that sensor data can be correlated and analyzed.

By adding a new dimension to existing IT information, sensor data is making it easy to predict and identify issues before they occur, and enabling organizations to make decisions in real time.

At most industrial organizations today, the lack of real-time visibility into critical industrial systems causes a reactive approach to managing operations. Problems are often solved via intuition, rather than embracing a data-driven approach. In these environments, unplanned equipment failure and system downtime costs a manufacturer millions of dollars in lost revenue and business opportunities.

As an example, let’s look at a wind farm using a reactive versus proactive maintenance for its wind turbines. Using a reactive approach, the wind farm’s strategy is geared around fixing problems instead of preventing them. By choosing to forgo a proactive, data-driven strategy, the wind farm could lose millions of dollars from failures and unplanned maintenance. One estimate is that 1% to 3% of turbines require blade replacement annually, and according to an NREL analysis, the cost of each blade is approximately $150,000. If 100 blades were replaced each year, the cost would be around $15 million. In reality, that loss of revenue is higher due to the cost of blade installation and possibly hundreds of other asset failures to address.

Because most IoT systems use real-time machine data, analytics can be used to calculate equipment health and efficiency, so organizations can predict and prevent failures before they happen. Machine learning and customized statistical models enhance predictive maintenance efforts by diagnosing alarms and anomalies in real time, accelerating issue response time without affecting production.

Getting a simple view of complex industrial data is priceless. It takes away the hefty price organizations pay for maintenance and unplanned downtime. By taking an analytics-driven approach to maintenance and integrating data across disparate industrial control systems, sensors and applications, organizations can eliminate data silos and shift from a reactive to proactive method for managing operations.

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