Diesel Particulate Filters (DPFs) failures don't start with a loud alarm. They creep in as subtle shifts in differential pressure and regeneration behavior. In this transit predictive maintenance case, the vehicle's computer never warned of a DPF problem during the lead-up, but WatchTower flagged it one week before a diagnostic trouble code (DTC) and before the filter had to be replaced. The signal: steadily rising DPF differential pressure despite completed regens. This is predictive maintenance for DPFs done right: AI plus diesel expert validation, in time to act.

The Problem: Hidden DPF Risk in Real-World Duty Cycles

Aftertreatment keeps fleets emission compliant, but if any piece underperforms, you invite derates, road calls, and expensive downtime. The DPF is a major trouble spot even with regular regens, ash buildup or sensor issues can escalate quickly. Operations can't rely on late fault codes. They need earlier signals to prevent the downtime.

The Goal: Detect Failing DPFs From Live Operating Data

The objective was to build a model that detects a failing DPF using on-road sensor data, including during regeneration events, not just shop diagnostics after the fact. We wanted to be an early warning system to increase uptime.

The Approach: Context-Aware AI + Master Tech Labeling

WatchTower continuously ingests temperatures, pressures, vehicle speeds, and regeneration states while buses are on route. Because DPF metrics vary by operating condition, simple thresholds aren't reliable. Instead, the system combines trend analysis, regen analysis, and operating-context standardization and every alert is validated by a Master Diesel Technician who issues the repair plan and labels the event ("failing" vs "normal"). That feedback loop continuously improves accuracy.

The Result: A Week of Lead Time (And Often More)

In this case, WatchTower correctly identified a failing DPF in the week leading up to the failure, days before the ECM threw a DTC. Across deployments, fleets commonly see detection ~5 days in advance (and up to 30 days) with a 91% true positive rate when repair plans are issued. Translation: when we say act, you can trust the action.

What triggered the alert here? A steady climb in DPF differential pressure even with four complete regens in the prior week. Classic early warning that a DPF is on a bad trajectory.

What We Analyze to Predict DPF Failures

• Differential pressure trends across different operating states.
• Regeneration profiles: warm-up, duration, frequency, and effectiveness.
• Operating context standardization so each bus is compared to its own "normal," not a one-size-fits-all threshold.

Why Not Wait for Fault Codes?

Because they're often late. In this very case, the vehicle's computer did not flag anything wrong until after WatchTower had already called out the failure trend. Typically, these alerts come about 5 days early with a 91% true positive rate before being validated by experts, giving maintenance teams time to schedule work and avoid service disruptions.

How Fleets Put This to Work in 30 Days

1. Connect and baseline: Let WatchTower learn each vehicle's normal regen and pressure behavior from live routes. We can use your existing telematics or provide ours for a trial.

2. Act on expert-reviewed alerts: Every prediction comes with a repair plan and a technician's validation to reduce noise and indecision.

3. Measure impact: Track reduced road calls, fewer derates, and material cost avoidance as early warnings prevent emergency DPF replacements.

Ready to See It on Your Fleet?

If you manage a bus or coach fleet and want to get ahead of DPF failures, let's model your vehicles and run a pilot. You'll see how transit predictive maintenance, powered by AI and Master Technician review, translates into fewer surprises and lower total cost. Fill out this form, and we'll also send you a case study so you can see WatchTower in action and learn how real fleets are reducing downtime before failures occur.