Maintenance decisions often involve choosing between replacement and repair. The shortage of essential replacement parts has led to increased exploration of repair methodologies. However, repairs are often imperfect, leading to additional uncertainties in predicting the component's future condition. Existing approaches in the literature for modeling imperfect repairs struggle when repair dynamics are unknown requiring a large amount of data to be reliable. Furthermore, current methods are very task-specific, which limits the optimization of maintenance planning of varying components. This research addresses these challenges by conceptualizing imperfect repair effects as a stochastic increase in Remaining Useful Life (RUL). An existing deep learning model extracts prognostic-related features that can be utilized by any prognostic model to estimate RUL based on sensor data. Then, the proposed imperfect repair model predicts the RUL increase post-repair. This method offers three key benefits: (i) proactive post-repair assessment for improved maintenance, (ii) a data-driven repair model compatible with existing prognostic models, and (iii) flexibility in adapting to different repair techniques. Evaluation of the proposed model is conducted through tension-tension fatigue experiments on aerospace-grade aluminium specimens subject to imperfect repair. Results demonstrate the model's ability to accurately estimate the post-repair stochastic RUL increase.

Prognostics and health management (PHM) in aviation systems aim to predict remaining useful life (RUL), enhancing reliability, while considering operational uncertainties. Hidden Markov Models (HMMs) model degradation processes when damage states are unobservable, using representative features from condition monitoring (CM) data. Traditional HMMs struggle with geometric decay in hidden state durations, leading to the introduction of hidden semi-Markov models (HSMMs), albeit with increased computational complexity. This study compares HMMs and HSMMs, while introducing a dynamic prognostic expression. Using NASA's C-MAPSS dataset, encompassing diverse flight conditions and simulated engine failures, we validate the superiority of HSMMs over HMMs. Moreover, our novel time-dependent prognostic expression outperforms standard ones, highlighting its effectiveness in RUL prognosis.