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This is the second in a two-part series on predictive maintenance for circuit breakers. In my first post, I talked about a condition-based approach using real operational and environmental inputs. These can include mechanical and contact wear, as well as humidity, salt, corrosive gases, vibration, and other influences. Analyzing this data helps maintenance teams predict the actual health of each breaker related to its aging.
To put condition-based analysis in familiar terms, think about the tires on your vehicle. How much life you can expect from them? To estimate this, you really need to take into account more than distance driven. Other factors like speed, road conditions, and temperature all play a role in how fast they wear. The same concept is true for circuit breakers. You need to take into account more than just the number of operations to determine when servicing or replacement is needed.
Rather than following the interval-based scheduling of preventative maintenance schemes, using prediction allows for a more fluid approach. Maintenance can be performed only when it’s needed, while breakers needing more immediate attention can be identified. This can not only improve facility safety and reliability, but also boost breaker performance and lifespan. Along with reduced service time, this translates to lower OPEX and CAPEX.
So where does all this data come from? IoT technologies are making many types of facility equipment and devices smarter.
Some circuit breakers, such as the MasterPact™ MTZ series from Schneider Electric, now have embedded intelligence that is able to report on a wide range of operational parameters. This not only includes operation counters, but also interrupted current for each operation, contact wear, and electrical load data including harmonic content. Communications links enable this data to be continuously uploaded to a central power management database. Other smart sensors across a facility can provide real-time data on all environmental parameters. This type of data is typically made available from a process or building management database.
The next step is to make sense of all this data. Some power management applications, such as EcoStruxure™ Power Monitoring Expert, are now offering analytic capabilities specifically designed for breaker aging analysis. The software integrates all measured operational and environmental data to determine relationships between different aging factors.
From this analysis, failure risks are modelled. This helps maintenance teams determine the health of each breaker and how its current condition could affect the performance of the facility infrastructure. Typically, the system will provide regular reporting for all breakers, as well as a real-time view of breaker status, helping teams make decisions on service actions.
Reports can categorize breakers based on levels of associated failure risk. Teams can then focus corrective maintenance on devices at highest risk, ensuring service continuity. Based on levels of risk and other data, maintenance schedules can be set for each group of breakers to maximize efficiency. For lowest risk breakers, the team might postpone a previously scheduled inspection, if local regulations allow for such a decision.
If reports show conditions are changing over time, the team can optimize their schedule to respond to actual changes in predicted breaker aging. For conditions that are highly variable, the system will identify critical conditions needing immediate attention, sending an associated alarm to the mobile devices of maintenance personnel. They can then use an HMI display to isolate the problem breaker, drilling down to see more information on the specific conditions putting it at risk, such as a sudden increase in ambient temperature. Action can then be taken to service or replace the device.
Ultimately, using a condition-based predictive model can help tailor maintenance to the specific conditions and aging of each breaker. In demanding, harsh environments it can help reveal urgent situations and, in turn, help ensure safety and increase mean-time-between-failures of the electrical infrastructure. In less demanding applications, it can help extend breaker life while also reducing the number of inspections (depending on regulations).
By having smart breakers and associated analytics in place, maintenance can be adjusted in a dynamic way to conform to changing conditions and needs of the facility. This continuous optimization will boost productivity and help control costs. And in terms of managing time and cost, analysis and reporting can be managed onsite by the facility team or outsourced to a service provider that offers the relevant expertise.
For a deeper discussion of this subject, read ‘How predictive maintenance for circuit breakers optimizes safety, reliability, and costs’.
