Tank Fire Suppression Systems

Team NoSmoke didn't come to build another alert system. They came to solve a maintenance crisis—when M1A2 tank fire sensors misfire, maintainers waste time, money, and resources replacing components blindly.

What's At Stake

In the M1A2 Abrams tank, false fire suppression activations create operational chaos. When sensors trigger incorrectly, maintainers face a critical knowledge gap: they don't know which sensor misfired. The standard response is to replace all sensors, even when the real problem might be a loose wire or engine overheating. This wastes time, parts, and keeps tanks offline longer than necessary.

In the Field Learning

Led by Sabine Loaiza Chable, Team NoSmoke recognized that tank maintenance problems aren't solved from California—they're solved where maintainers work under pressure. Their breakthrough came during a 48-hour sprint at Joint Multinational Readiness Center (JMRC) in Germany, where they completed 30 interviews with tank crewmen, mechanics, and officers.

The trip revealed the core issue: no data was being collected or digitized from fire suppression events. Maintainers were operating blind, with no historical patterns to guide troubleshooting. The team saw firsthand how cramped tank interiors are, where sensors are positioned, and how suppression levers can be accidentally triggered.

Data-Driven Solution

Team NoSmoke developed a dual-component system addressing both immediate needs and long-term intelligence. Their real-time alert system displays sensor-specific notifications on the driver panel—the natural integration point that already handles alerts. Meanwhile, a mobile-accessible database captures suppression events, engine temperatures, and operational context.

Field testing with 30 interviews validated their approach: 8 maintainers specifically wanted sensor identification capabilities, while 5 prioritized engine temperature monitoring. The design fits existing tank workflows with minimal disruption but maximum clarity for troubleshooting.

Operators of Another Kind

Sabine Loaiza Chable, leading the effort from UC San Diego, brought systems engineering thinking to combat vehicle maintenance. Working alongside teammates Sally Liang and Andrew Doan, the team combined technical development with field-validated user research.

What Comes Next

Team NoSmoke's prototype lays groundwork for machine learning integration that could analyze false activation patterns and predict failures. Their system addresses an immediate maintenance pain point while building the data foundation for predictive capabilities.

They didn't just theorize about sensor problems. They got inside tanks and solved them.