How drives can help with predictive maintenance
Modern low-voltage drives are able to offer predictive diagnostic capabilities for manufacturers and reduce downtime.
Downtime is the single largest source of lost production time. An hour of unplanned downtime is often many times more costly than replacing the equipment related to the issue. This is particularly true of modern drive systems which are core to manufacturing and production. Drive performance is vital in achieving consistent outputs, and any deviation or failure has the potential to shut down a typically linear production platform.
Making choices with drives
There are several choices for managers when dealing with the lifespans of drives. The traditional route is to simply react to component failures when they occur or replace components on a fixed schedule that does not account for use and environment. The first approach can be costly and unpredictable as component failures are likely to cause unplanned downtime. The second approach is better, but is still not optimal.
However, today’s digital drives are able to utilize computing power to provide predictive maintenance capabilities to allow components to be replaced before they expire. This reduces unplanned downtime.
The value of predictive maintenance
Predictive maintenance models are built around a common framework where the amount of life consumed by each component is tracked by the drive. Advanced physics-of-failure models are incorporated into today’s drives to convert actual stressors such as voltage, current, speed, switching frequency, and temperature into life consumption for critical components like fans, power semiconductors, capacitors, and breakers. When the consumed life exceeds the user-defined event level, an alarm is generated indicating that preventative maintenance is required for the specific component.
In modern drive solutions, the rate of life consumption is tracked in the firmware for each component on a virtual rolling average. This rate is unique to each drive and depends on how it is used. Based on the rolling rate of life consumption, the new predictive models will calculate how many hours remain until the percentage of consumed life reaches the alarm level.
The intent of the event level, or maximum life consumed before an alarm is generated, is to allow the end-user to control the risk of unplanned downtime. Finally, as the new predictive maintenance algorithms adapt to how the drive is being used, it will take about 30 days for the models to learn about the application stresses.
Cloud-based CMMS system benefits
Utilizing data from intelligent devices within cloud-based computerized maintenance management systems (CMMS) make it possible to have a structured record of plant conditions and develop optimized maintenance programs to further maximize uptime and availability. The result is comprehensive and dynamic condition-based data based on both historical performance and actual device condition. By optimizing maintenance programs, not only uptime and availability are improved, but the lifespan of monitored assets are also enhanced so enabling maximum return on investment.
Original content can be found at Control Engineering.