The Internet of Things (IoT) has made our world even more connected than before. With the introduction of Industrial IoT (IIoT), we can expect the same for the industrial sector. Currently, IIoT is being used to increase efficiency in industries, particularly energy efficiency, via innovative solutions.
One such innovative solution is MiDAS from Tantiv4, a power analytic solution powered by artificial intelligence (AI) and machine learning (ML.) MiDAS is not only helping reduce energy consumption but also reducing downtime for manufacturers through smart, predictive maintenance.
Manufacturers are using MiDAS to overcome the greatest challenge of maintenance for manufacturers - equipment failure or breakdown and the resulting costly downtime.
Let's discuss reducing downtime in manufacturing using IIoT power analytic solutions like MiDAS for smart, data-driven, predictive maintenance.
How to Reduce Downtime in Manufacturing
Manufacturers of all sizes are constantly looking to enhance their processes more efficiently. The smallest of improvements can have a significant impact on their bottom line.
Manufacturers typically use one or more of the following approaches to equipment maintenance.
- Reactive Maintenance
Equipment maintenance and repairs are implemented upon failure.
- Cyclical Maintenance (Time-Based)
Equipment is routinely checked and maintained on a fixed schedule.
- Preventive Maintenance
Preventive checks and maintenance are implemented ahead of time to minimize equipment failure.
Regardless of the approach, equipment failure and breakdown still occur because many variables are hard to control. Here, predictive maintenance can help manufacturers optimize maintenance and reduce downtime.
- Predictive Maintenance
- Predicting defects, failures, and breakdowns before they happen, using IoT devices, sensors, and data analytics.
Many large manufacturing plants are moving to predictive maintenance because of its incredible efficiency and highly cost-effective nature, especially compared to other maintenance approaches.
For example, MiDAS uses IoT devices and sensors to identify faulty equipment by monitoring harmonics, temperature, and other equipment metrics. It uses data analytics to predict things like misalignment and wear out.
Using historical equipment data and benchmarks, MiDAS can accurately identify any hidden losses in the energy consumption or production efficiency of the equipment. This can highlight issues and produce actionable insights that trigger maintenance efforts.
Such a proactive, predictive maintenance approach can help reduce downtime in manufacturing by a significant amount. Moreover, since equipment maintenance occurs before failure or breakdown, the lifespan of costly equipment also increases.
Manufacturers no longer have to spend time, effort, and other resources on routine, time-based, or preventive maintenance. They can purely rely on the efficient predictive maintenance approach for their equipment.
Conclusion
Predictive maintenance is the future of maintenance in the industrial sector. IoT power analytic solutions like MiDAS can help reduce downtime in manufacturing and the overall cost of maintenance. Most importantly, they don't have to incur the heavy losses caused by equipment failure and operational downtime.
If you want to learn more about reducing downtime in manufacturing or the features, benefits, and convenient Software as a Service model, please visit MiDAS Power Analytics Solution today.