The customer is a national network of storage facilities offering tenants insurance for damage caused by rodents, water, fire, freezing, or overheating. Local maintenance personnel manage multiple sites, focusing on HVAC units and outdoor AC systems, which are the most expensive equipment over $700 per full replacement. These units can last years, but often fail during extreme weather, requiring urgent repairs to minimize damage.
By the numbers: What Viam enabled for the storage chain
15%
decrease in the maintenance budget
50%
reduction in unplanned downtime
80%
decline in unplanned downtime projected after the first year
The Challenge

Costly, Unpredictable, and Disruptive Machine Repairs

HVAC failures are costly, unpredictable, and highly disruptive. First, identifying a failure quickly is challenging, since property managers aren't always on-site, leading to multi-day detection delays. Second, calling a technician is often expensive and time-consuming, so after basic checks, units are usually replaced without clear diagnosis, even if it’s an excessive solution. Third, once failure is identified, replacement is urgent, forcing a costly fix.

The customer needed a way to identify unit issues before failure. They wanted to:

  • Immediately notice failure probability, with alerts
  • Access diagnostics to inform decision-making about less costly repairs
  • Have advance notice to order, ship, and deliver repair parts
  • Perform fixes before total failure, extending equipment lifespan and performance

While diagnostic visibility is common in large commercial HVAC units, it’s rare in smaller, residential-commercial hybrids. Existing solutions were expensive, complex due to hardware specificity and unpredictable connectivity, and required cost-prohibitive installation investments for their margin-sensitive business.

the solution

Flexible, Practical, and Affordable Predictive Maintenance

Viam offered a flexible, practical proposal for an extendable HVAC diagnostic solution:

  • Self-powered sensors: Electrical current sensors measure a unit’s current usage and are powered through the electrical field from the wire itself, functioning seamlessly in low-connectivity environments and eliminating the need for external power sources.
  • Long-range data transmission: The sensors use LORAWAN technology to transmit data, making it possible to broadcast signals miles away from the base station, an effective solution for managing widespread locations.
  • Reliable data collection: Data is gathered in the facility’s office area and uploaded to the Viam cloud via a viam-server, installed on an affordable Raspberry Pi. Data collection functions seamlessly even amidst intermittent connectivity, with automatic syncing once connection is restored.
  • Fast & simple hardware integration: Viam’s plug-and-play flexibility ensures seamless sensor installation with existing hardware, minimizing the need for expensive upgrades or specialized maintenance, saving time and costs.
  • Streamlined application development: Viam’s standardized APIs and robust security infrastructure simplify the development of applications for managing HVAC fleets across multiple facilities. Developers are relieved from building data management, security, user authentication, and data syncing features.
  • Integrated AI & machine learning: Viam includes native machine learning capabilities, creating a fast path to enhanced diagnostic precision, without third-party integrations. Over time, the models can get smarter and predict issues with filters, boards, engines, and thermostats with increasing accuracy.
the outcome

Viam’s innovative solution unlocked a new approach to predictive maintenance that’s scalable, efficient, and precise. Critical operational improvements include:

  • New visibility to achieve timely failure detection
  • Improved diagnostics to distinguish issues and support cost-effective repair decisions
  • Increased mean time to failure (MTTF) due to proactive maintenance
  • Expected benefits at scale, such as optimized inventory management for repair parts
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