Evaluation

• The effectiveness of the developed algorithm was assessed by measuring the time lag between the initial alert generated by the system and the subsequent appearance of the corresponding fault code. This evaluation method demonstrates the algorithm’s ability to provide early warnings ahead of actual fault occurrences, enabling proactive maintenance and reducing downtime.

Uniqueness

• Forecasting as Classification:
  Unlike traditional forecasting methods, I approached the problem as a classification task. By deeply understanding the physics of the DPF, I recognized that predicting clogging events ahead of time is possible, despite the absence of clear, gradually evolving signatures in the differential pressure time series data.

• Why This Matters:
  This perspective was necessary because the differential pressure signals do not show smooth trends or gradual changes before clogging. Instead, the model classifies regeneration event patterns to predict the likelihood of an upcoming clogging event, offering a more reliable and interpretable early warning system.