# Prescriptive Maintenance: A Systematic Literature Review and Exploratory Meta-Synthesis
## 4 Discussion
### Applications, Methods, and Tools  
The review reveals that PsM is concentrated primarily within manufacturing, energy, aerospace, and railway domains, where downtime is costly and asset longevity is critical. The prominence of these sectors reflects the maturity of the condition monitoring infrastructure and increased convergence of maintenance with production planning and control [22; 59; 60; 61]. Although heavily relying on expensive waste-energy sensor data (e.g., temperature, vibration), many are considering alternatives of inexpensive functional-primary power sensors (e.g., flow, pressure) [56]. This translation aligns with an increased focus on physics-driven modelling and causal machine learning, provoking renewed debates on the value of interpretable, context-aware decision models in maintenance [55]. Still, current applications remain anchored in data-driven frameworks, particularly predictive models for prognosis and optimisation of subsequent maintenance actions. In terms of methods, the PsM landscape is dominated by supervised ML models (Table 3), such as Random Forest (RF), Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs) [62; 63; 64]. RF is especially prevalent due to its high classification accuracy and ability to perform feature importance, making it well-suited for root cause analysis and anomaly detection. The models are typically trained on historical failure data or sensor data, which is commonly required to forecast remaining useful life (RUL) or classify failure types. Complementing ML, the DL models (Table 4) have significantly expanded the scope of prescription for PsM. Namely, based on reviewed studies, key DL architectures--LSTM, CNN, GAN, ESN, and Reinforcement Learning (RL) variants--have been deployed to meet operational targets in PsM frameworks: (i) temporal dependencies and prognosis (LSTM, RNN, ESN) for practical RUL estimation and the ability to model long-term dependencies in time-series data. Dual-LSTM architectures demonstrate high accuracy and real-time feasibility in complex machinery, such as turbofan engines and batteries [72; 75]. In contrast, ESNs are suitable for adaptive modelling in environments with noise and constraints of real-time systems [62]. (ii) Spatial-Temporal Feature Extraction (CNN, DNN) is used in extracting spatial features from vibration and thermal data processing for early detection and RUL prediction [64; 78]. (iii) Causality and Reasoning (MLP-ANN, ML causal learning) are gaining significant prominence. Causal ML, addressed by [PERSON] et al. [55], leveraged MLPs in combination with causal inference to predict individualised effects and optimise maintenance schedules, thus handling heterogeneity across datasets. (iv) Generative Learning and Anomaly Detection (e.g., GAN) in the PsM domain are usually employed to simulate rare failure events and generate synthetic training data for imbalanced datasets. GAN is beneficial in classification problems, particularly in cases where failure samples are rare or costly to collect [55; 81]. (v) Action-oriented Optimisation (Deep RL)--Deep RL and Transformer networks are becoming an integral part of PsM applications, specifically decision-making layers, by recommending optimal maintenance actions by balancing cost, settings, and operational constraints [9; 82; 83; 84; 85]. Although commonly used for making cost-effective decisions, [PERSON] et al. [64] demonstrate the applicability of adopting sustainability in PsM decision-making. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Study** & **SVM** & **ANN** & **RF** & **DT** & **kNN** & **NB** & **AdaB** & **kM** & **XGB** & **PCA** \\ \hline [65] & ✓ & ✓ & ✓ & ✓ & ✓ & & & & & & \\ [66] & & ✓ & & & & & & & & ✓ \\ [67] & & ✓ & & & & & & & & \\ [68] & & & & & & & ✓ & & ✓ & \\  
Although Deep RL has been established as a core component in PsM, the interpretability of models, particularly in safety-critical and industrial applications, becomes a significant challenge.