Changing building maintenance from prevention to just-in-time achieves tremendous improvements in efficiency. It reduces labor by at least 30%, cuts energy by an average 12%, and improves uptime by 90%.
Results like these depend on taking the guesswork out of building maintenance. We know how to make that happen. Thousands of buildings prove it.
Traditional building maintenance includes scheduled preventive measures and maintenance staff walking around trying to locate the problems. Maintenance schedules undoubtedly swap out equipment that still has useful life. Cruising around the building to try to locate the source of problems chews up labor hours.
Today, technology can replace this traditional approach with a far more efficient, effective and flexible model. The fashionable term is Big Data, which in this context means evaluating information from all the different sensors in a building to optimize its operations.
People are beginning to apply the Big Data buzz word to building performance without truly understanding the value that it brings. Although almost everyone talks about the benefits in terms of better energy conservation, that is actually a minor part. Big Data’s true value is enabling a far more effective maintenance solution that delivers verifiable, hard-number savings by reacting just in time to problems exactly as they happen with exactly the right response.
In 2007, I began experimenting by getting raw data from computerized control systems in commercial buildings, and running mathematics to determine faults. The concept was to get as much value as possible by extracting data from existing sensors without incurring additional cost.
Over the course of working on several thousand buildings, I discovered some key pain points. At the bottom of the Big Data food chain are the sensors that generate the data. The challenge is that the values registered by current-generation sensors are not very accurate, and Big Data becomes Big Garbage when the values that you gather are not accurate.
We have a two-pronged solution to the sketchy sensor challenge. The first prong is a three-part process that qualifies the data from existing sensors to make sure they are working within a reasonable parameter of what they ought to be; determines whether there is a fault in the equipment’s operation; and predicts when the machine is going to have a fault or failure.
The second prong is a new generation of low-cost sensors that automatically self-calibrate to maintain and communicate accurate measurements directly to the building’s computer system. Their reliably accurate data, combined with continuous analysis and predictive technology, further refines the ability of maintenance staff to know where a problem is occurring and what to do about it.
Our new water-flow monitor, the iCaptor, is the first of these next-generation sensors, and we are working on others that address all of the pain points that we have experienced.
Today we can provide the maintenance staff with a daily list of things for them to do based on the actual state of the building’s operational systems. Over the next few years, as building owners replace their outmoded sensors with our low-cost/high-return, next-generation monitors, the assessment will be progressively more effective and efficient. That will further improve the productivity of the maintenance effort, and the profitability and energy profile of the building.