Douglas West, director of product management at Stirling Ultracold, explains how predictive diagnostics built into the control system of the company’s VAULT100 freezers, designed for temperatures down to -100°C, are helping labs shift from reactive maintenance to more informed, proactive decisions.
Q: What are the biggest challenges labs face in maintaining their ultra-low temperature (ULT) freezers?
A: The top concern is sample integrity—full stop. But labs also face pressure to reduce downtime, avoid unplanned maintenance, and manage operating costs. When you step back, it all ties into one theme: maximizing total cost of ownership without compromising reliability or sustainability.
That’s where our work on the VAULT100 really focused. We improved temperature uniformity and energy efficiency, hitting a record ACT Label score of 23 for upright freezers while keeping the same footprint and cost structure. That means better sample stability and lower lifetime energy use, without forcing labs to overhaul their infrastructure.
At the same time, we’re seeing growing environmental awareness. Labs want freezers that perform and align with their sustainability goals. That includes everything from natural refrigerants to longer lifespans. With fewer failures, you reduce waste—not just in parts, but in full-unit replacements. That’s where design choices really matter.
Douglas West
Q: How is predictive analytics changing the way labs manage freezer reliability and sample safety?
A: There’s an old saying: the windshield is bigger than the rearview mirror. That’s how we approached predictive analytics. Most freezer systems look backwards—they alert you after something has gone wrong. We wanted to look forward.
With onboard predictive analytics, we’re able to read the health of the freezer in real time. Not just temperature alarms, but subtle changes in engine performance that tell us when something’s drifting out of spec. That lets lab managers act early before a failure happens and before samples are at risk.
It’s a mindset shift: from reacting to predicting. From scrambling to plan-ahead. And because we built this system around the Stirling engine—which is mechanically simpler and more stable than compressor-based systems—we can track fewer variables more precisely. It’s a cleaner signal, and we’ve built the tools to make sense of it.
Q: What’s the difference between standard predictive analytics and Stirling’s onboard system?
A: Most predictive systems are bolt-ons. They rely on external sensors to monitor conditions like room temperature, door openings, or temperature excursions. They’re useful, but they’re still reactive. You define thresholds, and when something crosses the line, you get an alert.
Our system is different because it’s embedded. We don’t need external sensors—we pull performance data straight from the freezer’s control board. That includes engine power, piston movement, heat rejection, and a few other key indicators. Then we apply proprietary logic, based on how our free-piston Stirling engine behaves under load.
And because each freezer is unique, each diagnostic result is unique too. Think of it like a health check. Two people might have similar symptoms, but different causes—and different recommendations. That’s how our system works. It’s not just alerting, it’s advising. Here’s what your specific freezer needs, based on how it’s operating today.
Q: Does that make it more complex for lab managers?
A: That was one of our biggest design challenges—how do we build something smart that doesn’t overwhelm people?
We ended up with a traffic light model. The system evaluates five core performance indicators and displays the result as green, yellow, or red. Green means everything’s in spec. Yellow is a heads-up—maybe it’s time to defrost, or check airflow. Red means it’s time to call service.
That way, even someone new to ULT systems can respond appropriately. And we don’t just throw a color on the screen: we include guidance on what to do next. It removes guesswork and lets scientists stay focused on their research.
Q: What specific performance indicators do you track?
A: We look at five areas: engine power, motor function, piston run, heat rejection, and thermosiphon performance. Each one reflects a different aspect of freezer health.
Of those, engine power is the most telling. It often signals the earliest sign of drift. Maybe insulation is degrading slightly, or ice buildup is putting extra load on the engine. We can catch those trends before they turn into problems.
Q: How are those benchmarks established?
A: Every unit is calibrated at the factory. From there, the system adjusts its predictions over time based on actual performance. It’s kind of like how your doctor tracks your health baseline as you age—we do the same with each freezer.
That means we’re not comparing your two-year-old unit to a brand-new one. We’re comparing it to itself, using real-world data. That makes the diagnostics far more relevant.
Q: Is this system built into every VAULT100? Or is it an upgrade?
A: It’s standard. Every VAULT100 comes with predictive analytics and preventive maintenance reminders built in. There’s no extra software, no additional license. You don’t need to connect to the cloud or download anything. Just tap the screen.
Q: How does this impact sample safety?
A: In a word: planning. If the system detects early signs of stress, labs can schedule service or even prepare a backup unit. That’s a huge difference from scrambling after a failure.
And with our new -100°C setpoint option, labs get an extra 20 degrees of thermal protection compared to standard -80°C freezers. That margin matters, especially during power disruptions.
Q: Where is predictive analytics headed next?
A: Right now, it’s all onboard—you read it at the freezer. But the future is cloud-connected. Imagine a fleet of freezers across multiple sites, all feeding into a central dashboard.
That unlocks predictive replacement cycles, energy optimization, and centralized oversight. It also helps labs plan budgets based on usage data, not just warranty timelines.
Q: Any final thoughts for lab managers exploring this tech?
A: Predictive analytics isn’t about adding complexity. It’s about removing surprises. Labs already have enough variables to manage. Our goal is to make cold storage one less thing to worry about.
If we can help protect your samples, lower your energy bill, and simplify freezer maintenance—all at once—then we’re doing our job.
