This work presents a data analysis-based, group-aware framework for predicting quality indicators with anomaly detection in non-i.i.d. datasets that exhibit short temporal dependencies. The design is motivated by statistical diagnostics of temporal autocorrelation and intraclass variance, which highlight the need for causal temporal encoding and group-level decomposition. The framework integrates a residual-boosted regressor, a group-aware anomaly detector, and a calibrated fusion scheme that balances precision and recall. Evaluation is conducted on real production data from hot strip mill operations, with coiling temperature prediction serving as a case study. A key contribution is interpreting coiling temperature dips, previously treated as outliers, as proxies for surface anomalies, thereby enabling their explicit detection. Results demonstrate consistent gains over physics-based and tabular machine learning baselines, confirming that the framework provides more reliable quality-risk indication for decision support in industrial predictive-control workflows.

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.

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.