Irregular Time-Series Hybrid Model for Enhanced Prognostics of Engine Degradation and Failures

Classification-based prognostics aims to predict the Remaining Useful Life (RUL) of components in diesel engines by identifying failure and degradation stages. This is critical for industries such as automotive, aviation, and manufacturing. Traditional methods rely on classification models trained on historical data from multiple engines to forecast failures based on current engine parameters. However, these global classifiers often struggle with generalization when applied to unseen engines, resulting in poor precision and recall. Moreover, they fail to capture the temporal dependencies inherent in engine degradation, which are crucial for accurate failure prediction. We propose a hybrid model that integrates predictions from global classifiers with time-based memory units to address these limitations, effectively building irregular time-series models. Our approach demonstrates a significant performance improvement, with precision and recall metrics doubling compared to traditional global classifiers.