5 Examples of Predictive Maintenance in Action

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A freight train is running at full force and making excellent time. On paper, everything looks great, with stellar performance and up-to-date maintenance records. But at track level, sensors tell a different story. A part needs repair, and if not addressed quickly it could result in breakdowns, delays or financial loss.

The tracks are equipped with sensors that are carefully collecting ultrasonic and vibrational data about the train’s performance. Those sensors are connected to the internet, and they can transmit that data to the appropriate parties within seconds. As a result, what were unexpected maintenance issues are predicted and addressed before a problem occurs, and the negative outcome is avoided.

Predictive maintenance is powerful and effective, and can eliminate up to 50 percent of maintenance-related exposure and extend asset life by up to 60 percent. What’s more, it helps companies move from a reactive to a proactive state of operations. Here are five ways this technology can help companies gain a stronger competitive edge.

1. Preventing Utility Outages

Utility outages are disruptive and expensive, with crews working around the clock to quickly identify, repair and resolve problems. The Internet of Things and predictive technologies are changing this paradigm by identifying costly problems before they occur.

Drones with equipment hooked to sensors can map utility networks and utilize machine learning to identify trees that are at risk of falling on power lines. By assessing the essential data at critical moments, this technology could reduce costs by up to 30 percent.

More efficient use of resources means that companies can reallocate these resources to make more frequent inspections, further reducing potential downtime.

2. Enhancing Safety and Transportation Operations

Internet-enabled sensors are allowing transportation systems to operate more efficiently, increase safety and reduce unexpected maintenance costs. Railway operators are already seeing the opportunities this presents, and it is estimated that nearly $30 billion will be spent in the next 15 years on IoT projects in the railway industry.

Data can be identified and reviewed across multiple transportation assets, allowing operators to identify problem areas and opportunities for maintenance before potential failures occur. For example, French rail company SNCF is using machine learning anomaly detection to drive insights from vast amounts of data. This is enabling them to significantly optimize their rail network.

3. Better Understanding of Efficient Workloads

Sensors also can be used in the shipping industry to monitor everything from engines to generators to air-conditioning systems and fuel meters. For example, one shipping company used sensors to better understand fuel meter readings and the power used in refrigerator containers.

Data collected by sensors painted a vivid picture, highlighting for example that running more generators at lower power was of greater efficiency than maxing out a few. As a result, the company saved money by not overworking existing generators, which would require more frequent repairs, but instead spread demands over more equipment. Savings were estimated at $30 per hour, and with a fleet of 50 ships that operate 24 hours a day, 26 weeks a year, the potential savings are $6.5 million.

4. Keeping Tabs on Temperature Readings

The temperature of a piece of equipment is of critical importance during the manufacturing process. Equipment that becomes unexpectedly overheated can compromise operations and result in unexpected delays, additional costs, and customer disappointment.  To avoid this, an infrared imager can evaluate the temperature profile of equipment without disrupting productivity.

5. Putting Your Ear to the Ground With Ultrasonic Analysis

The sensing of audio and visual signals empowers companies to uncover required repairs. Audio frequencies are measured from equipment and compared to existing data to determine potential problems, such as faulty electrical equipment, bearings that are not lubricated, or leaky valves.

Problems are quickly identified, data alerts are sent and repairs can be made without workflow disruption.

Empowerment through Data

Predictive maintenance offers the mechanisms to uncover issues that would have otherwise been very difficult to anticipate in the past. No longer are companies relying only on historical data to implement maintenance, but instead are receiving real-time data on the actual condition of their assets.

Safety, security and cost are all positively impacted when issues are dealt with in advance instead of after an issue has occurred. Predictive Maintenance is a game changer for companies as it enables greater efficiency and effectiveness, driving the potential of significant competitive advantages for those that can successfully implement it.

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