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IoT-enabled predictive maintenance

What is Predictive Maintenance?

Predictive Maintenance is proactively figuring out when an asset should be maintained based on its actual state, rather than on a fixed schedule.

The Need for Predictive Maintenance

Unplanned Outages
Unplanned equipment downtime can mean big losses in revenues and productivity. For example, some of the leading automotive manufacturers estimate that unplanned downtime can cost them as much as $15,000 – $20,000 per minute and a single downtime event can cost approximately $2 million.

Unnecessary work
Performing preventative maintenance regardless of the condition can lead to unnecessary costs and delay.

Fundamentally Reactive
Limited to no analytic modeling capabilities to prevent and predict issues before they impact.

How can IoT-enabled predictive maintenance transform your business?

In Virginia last year, an airplane was grounded by an unlikely adversary: a large swarm of bees. The peculiar story made for great newspaper headlines and serves as a reminder that even with the best technology and planning, some things are truly unexpected. But fortunately, most aircraft delays are caused by far more predictable issues than an unwelcome swarm of bees nesting in a turbine. Airlines, like most asset-intensive businesses, are getting increasingly better at predicting failures and anticipating maintenance problems. Rather than keeping planes grounded for costly and annoying last-minute maintenance — or, worse, exposing passengers to the risk of flying on a faulty aircraft — airlines are investing in cutting-edge technology that detects potential problems before they arise. Wouldn't it be nice if you could be alerted to equipment failures or maintenance issues before they happen? That’s where the real possibilities of IoT-enabled predictive maintenance is going to be useful.

IoT enables companies to solve problems before customers realize they exist, resulting in reduced downtime and maintenance costs, hence better customer experience. Machines and equipments are fitted with sensors such as temperature, pressure, vibrations/second, noise levels, etc. These sensors continuously monitor key attributes or performance indicators — such as temperature, pressure, vibrations/second, noise levels, etc. Utilizing IoT and sensor data from connected equipments, businesses can now gain visibility into the condition of their valuable assets and specific components in real time. Organizations are also using advanced analytics and machine learning to detect anomalies or patterns that are indicative of failure and intervene as soon as initial signs of failure are detected to perform the right maintenance activities. Early detection helps in reducing failures and costs.

Examples of IoT-enabled predictive maintenance

For a great example of how IoT-enabled predictive maintenance can transform business, let's look at a steel manufacturer with multiple plants. Each plant has multiple arc furnaces that use water cooling panels for temperature control. However, leakages in the panels were causing safety issues as well as production losses. To resolve this issue, the manufacturer build IoT solution that remotely monitors the panels, detects anomalies, and performs root-cause analysis. The implementation of predictive maintenance has prevented failures and production delays throughout the plants while helping ensure employee safety.

In another example, aircraft engine manufacturer Rolls-Royce implemented predictive maintenance on IoT platform to help their customers reduce costly flight delays caused by engine maintenance issues. Each of their 13,000 engines in operation worldwide has thousands of sensors that monitor engine components and deliver insights around fuel efficiency, engine performance, and operational efficiencies. These insights enable Rolls-Royce to anticipate maintenance needs and avoid costly, unscheduled delays.


In short, the IoT wave has simplified plant equipment maintenance, while externally it has enabled manufacturers to enhance customer support services.

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