# An EWS-LSTM-Based Deep Learning Early Warning System for Industrial Machine Fault Prediction
## 2 Theoretical Background
### Early Warning System
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Model Adopted** & **Application Area** & **Key Findings** \\ \hline
[41] & Landslide Early Warning System (EWS) & Geotechnical Monitoring & Effective design and implementation of an EWS for landslide prevention, improving hazard mitigation. \\ \hline
[42] & IoT-based EWSs & Various Sectors & IoT technologies enhance the efficiency of EWSs, enabling real-time monitoring and data-driven decision-making. \\ \hline
[43] & Computational Intelligence & Cyber–Physical Systems & Computational models improve decision-making in natural intelligence-based early warning systems. \\ \hline
[44] & Internet of Things (IoT) & Smart Manufacturing & IoT integration enhances predictive capabilities and automation in industrial monitoring. \\ \hline
[45] & Cyber–Physical Systems (CPSs) & Industrial Monitoring & CPSs improve system reliability, enabling real-time data analysis and fault detection. \\ \hline
[46] & Smart Analytics \& Industry 4.0 & Manufacturing and Big Data & Advanced analytics enhance predictive maintenance and anomaly detection. \\ \hline
[47] & DFA (Detrended Fluctuation Analysis) & Electronic Component Monitoring & DFA provides early warning signals for predictive maintenance of electrical resistance systems. \\ \hline
[48] & ACF (AutoCorrelation Function) & HVAC System Monitoring & Regression and clustering techniques improve performance benchmarking and anomaly detection. \\ \hline
[49] & Invariant-based Identification & Lithium-Ion Battery Monitoring & Invariant learning enhances multi-stage identification of battery performance degradation. \\ \hline
[50] & Data-Driven Model & Heat Pump Performance Monitoring & Data-driven approaches effectively detect operational anomalies in air-source heat pumps. \\ \hline
[51] & Fuzzy Logic-Based EWSs & Predictive Maintenance & Fuzzy logic improves condition-based maintenance strategies. \\ \hline
[52] & Deep Learning (DL) & General Predictive Analytics & DL models are highly effective in detecting complex, non-linear patterns in EWS data. \\ \hline
[53] & LSTM Networks & Industrial Anomaly Detection & LSTM-based models improve long-term dependency learning, aiding in rare event prediction. \\ \hline
[31] & LSTM–Bayes Model & Steam Turbine Maintenance (Nuclear Sector) & LSTM–Bayes model enhances predictive maintenance for nuclear power turbine units. \\ \hline
[10] & LSTM-Based EWSs & High-Speed Train Axle Box Bearing Monitoring & LSTM improves detection of anomalies that traditional sensors fail to identify. \\ \hline
[54] & LSTM + Bayesian Inference & Machinery Anomaly Detection & Hybrid model optimises fault prediction and decision-making in industrial settings. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis of models, applications and key findings. Compared to traditional methods, LSTM networks offer a significant advantage in handling sequential data, improving the predictive capabilities of EWSs and reducing false positives. However, some challenges remain, including the need for large volumes of training data and the computational complexity associated with implementing such models. In light of these considerations, this study proposes the development of an EWS based on LSTM networks, evaluating their performance compared to other available approaches. The following section will present a case study demonstrating how the theoretical capabilities of LSTM networks translate into tangible benefits for predictive monitoring in the manufacturing sector.