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Maintenance strategies have evolved dramatically over the past few decades. Enterprises typically start with a reactive approach, addressing failures after they occur. Over the years, this has matured into preventive methodologies with fixed schedules. The next step in this journey is Condition Based Maintenance (CBM). This powerful approach is enabled by combining static, spatial, and live data with time context in platforms like Willow to bring intelligence and automation to the forefront of operations.
Reactive Maintenance, sometimes referred to as ‘run-to-failure’, is the most basic approach where facilities use equipment until it fails, and only then is it repaired or replaced.
Imagine a university lecture hall where a problem Air Handling Unit (AHU) goes unnoticed during the peak of summer. As temperatures soar, the aging AHU fails, impacting indoor comfort for students and faculty. The failure disrupts classes and strains other systems in the building trying to compensate. In response, emergency repairs are rushed. Weekend labor, expedited parts, and temporary cooling units all come at a premium cost.
The scenario occurs across domains. Now, let’s imagine an airport that has a slow, undetected water leak beneath a runway. Hidden below the layers of asphalt, this leak silently goes unnoticed for months. Moisture from the water gradually erodes the substructure, weakening the foundation day by day. Finally, on a rainy morning, a landing aircraft causes a section to buckle, forcing an emergency shutdown. Engineers discover extensive damage far beyond surface repair. The airport mitigates with rerouted flights and extensive reconstruction. If sensors were in place to detect moisture, the leak could’ve been addressed early. Instead, reactive maintenance let a minor issue progress into an operational situation.
While this approach creates the perception of being cost-effective in the short term, it frequently leads to unplanned downtime, higher repair costs, safety risks, shortened asset lifespan, and potentially ripple effects across systems.
To reduce the impact of Reactive Maintenance, organizations often adopt Preventive Maintenance, sometimes called Scheduled Maintenance. This involves servicing equipment at regular intervals regardless of its condition. While this approach is certainly better than being completely reactive, it still has inefficiencies.
Take filter replacement as an example. Facilities replace thousands of filters monthly based on a fixed schedule. But are all of them truly dirty or clogged, or are they being replaced simply because the calendar says so? For a large university campus with 137 AHUs, each with10 filters, that’s 1,370 filters to be change each month, or 16,440 filters per year, many of which may not actually need replacing. A large hospital may have up to 150 AHUs, depending on its size and specialization. Hospitals often use higher-grade filters (MERV 14–16 or HEPA) and may have more filter stages per AHU. With 150 AHUs and 12 filters per unit, that’s 1,200 filters potentially replaced monthly 21,600 filters per year. Large airports have hundreds of AHUs across terminals, concourses, baggage areas, and support buildings. A conservative estimate of 200 AHUs × 10 filters = 2,000 filters. Monthly replacement could mean 24,000 filters per year.
These numbers highlight the scale of potential waste. A time-based strategy leads to unnecessary labor and material costs, premature disposal of usable components, environmental waste and missed opportunities to optimize asset life. All in all, Preventive Maintenance reduces emergencies but can be wasteful and imprecise.
CBM uses real-time data and insights to determine when maintenance is actually needed. This approach leverages sensors, analytics, and skills to monitor the health and performance of equipment continuously. Referencing the example above, CBM asks the following questions: Has the filter differential pressure reached a threshold indicating clogging? Has the fan speed increased from baseline, suggesting excess static pressure? Are there vibration anomalies or temperature spikes that signal wear? CBM reduces downtime and maintenance costs by servicing equipment only when needed. It uses real-time data to predict failures, improving asset reliability and operational efficiency.
Willow enables data-driven maintenance decisions by integrating real-time data from equipment sensors and correlating it to static data like pre-defined schedules. Data from multiple systems is combined to generate insights by applying advanced analytics to determine optimal timing for maintenance tasks.
In this example, Willow takes into account the recommended monthly interval for filter replacement from the Preventive Maintenance schedule. Next, pressure readings from the filter switch analyzed with Activate Technology determine if the pressure drop exceeds a pre-determined threshold to make a recommendation for action. Lastly, data from a related system in the form of supply fan speed is compared against a baseline to indicate excessive static pressure drop. Combining these signals provide a more precise indication of when the filter change is actually needed.
One of the most compelling benefits of CBM is its ability to extend the useful life of equipment. Consider a 15-year-old AHU nearing the end of its expected lifespan. With traditional maintenance, it might be replaced preemptively. But with CBM, real-time data might show that the unit is still performing well, allowing it to operate for another 2 years, saving capital costs and reducing environmental impact. This results in increased operational efficiency with reduced maintenance costs, fewer emergency repairs, and improved asset reliability.
CBM helps achieve sustainability goals. By avoiding unnecessary replacements and optimizing energy use, this methodology contributes to lower carbon emissions, reduced material waste and smarter energy consumption. This helps organizations improve operational efficiency while meeting sustainability targets.
Evolving towards CBM requires a shift from traditional practices and training teams to interpret and act on data. The ROI is compelling and building a solution with Willow enables a path forward. Maintenance is evolving from reactively fixing what’s broken or following tasks on a schedule towards predicting what’s likely to fail and preventing it. With Willow, organizations can transition from reactive and preventive models to a Condition Based Maintenance approach. This transformation empowers teams to make smarter decisions, reduce costs, and extend the life of critical assets.
Whether you’re managing HVAC systems, water meters, or other building infrastructure, CBM with Willow is the key to unlocking operational excellence.