Fault detection improves fuel and emissions readings in diesel engines
Sensor Diagnosis for Fuel and Emissions Monitoring in Heavy-Duty Diesel Vehicles
Machine Learning
Summary
Faulty sensors in heavy-duty diesel trucks can give wrong numbers about fuel use and pollution. The authors tested two ways to spot and fix sensor problems by comparing sensor readings with healthy patterns they learned beforehand. Their method can detect many sensor faults correctly and fix fuel readings, cutting carbon dioxide calculation errors by over 80%. This helps get better data on emissions without messing up other sensor information.
What this means in practice
- •For fleet operators: Identify and correct sensor faults to improve fuel use and emissions monitoring in trucks during typical engine conditions.
- •For engine diagnostics teams: Use learned sensor relationships to diagnose specific sensor faults and improve emission calculations in diesel engine testing and maintenance.
- •For environmental compliance software developers: Integrate sensor fault detection and fuel correction algorithms into emissions monitoring software for heavy-duty diesel vehicles.$Commercial implications: Enables development of advanced software products that enhance emissions reporting accuracy for regulatory compliance in commercial diesel fleets.
Tested on one dataset.
Authors
Tejas Dahiya, Saurabh Kumar Gupta
Abstract
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.