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This paper introduces a two-stage procedure for diagnosing faulty sensors and correcting fuel consumption estimates in heavy-duty diesel vehicles, leveraging learned relationships from healthy sensor data. The diagnosis stage identifies faulty sensors by assessing the predictive improvement of replacing a suspect reading with an estimated healthy value, achieving 77.3% diagnosis issuance with 92.1% accuracy on injected faults. The fuel correction stage further refines CO2 emissions accounting by selectively replacing fuel readings when it maximally reduces prediction error, leading to an 83.8% reduction in mean absolute error for calculated combustion CO2 under simulated fault conditions.
Learned relationships from healthy sensor data enable highly accurate diagnosis and significant fuel correction, reducing CO2 emissions accounting error by over 80% in heavy-duty diesel vehicles.
Faulty sensor readings can distort estimates of fuel use and carbon dioxide emissions from diesel engines. We evaluate two procedures that use relationships learned from healthy measurements to diagnose faults and guide fuel correction. The diagnosis procedure tests whether replacing a suspected faulty reading with an estimated healthy value helps a model predict another sensor's recorded reading more accurately. It then predicts the fault type and decides whether to report the diagnosis. We evaluate five synthetic fault types on 217 steady-state operating points from a heavy-duty diesel engine, excluding all measurements at selected engine speeds from training. Across 31,500 fault-injected groups of 20 operating points, it issues diagnoses for 77.3% of groups, with 92.1% of diagnoses issued on these groups correct. The fuel correction procedure uses a different sensor-selection rule at individual operating points, testing how each replacement changes the largest prediction error across all six sensors. Fuel correction requires a fault alarm and engine speed and torque close to the training conditions. A fuel reading is replaced only when its replacement reduces the largest prediction error sufficiently more than replacements for the other sensors. Assuming faults in 1% of one-hour scenarios, equally likely sensors and fault types, and equal time at each measured condition, selected fuel corrections reduce mean absolute error in calculated combustion CO2 by 83.8%. On this engine map, learned relationships support complete sensor diagnoses and fuel corrections that improve emissions accounting while preserving unaffected readings.