# An EWS-LSTM-Based Deep Learning Early Warning System for Industrial Machine Fault Prediction
## 1 Introduction
The integration of EWSs and AI allows companies to move from reactive to predictive maintenance, anticipating problems before they occur and optimising the planning of maintenance activities. Despite their growing adoption, EWSs still face critical limitations. In particular, in the industrial context, one of the main issues lies in the event collection phase: systems must handle large volumes of heterogeneous data streams, often affected by noise and false positives, which compromise the effectiveness of subsequent analyses. Moreover, correlating events from disparate sources raises complex challenges related to semantic integration and the timely detection of rare or weak signals, all while ensuring security, scalability, and adaptability across different production environments. Lastly, activities such as anomaly forecasting, signal prioritisation, and visualisation demand advanced proactive solutions, which remain largely underexplored in the current literature [34]. These limitations highlight the need for more robust and flexible approaches capable of improving predictive accuracy, reducing response times, and enhancing the ability to distinguish relevant events from background noise. In this context, the research question that is addressed in this study is as follows: How can an intelligent early warning system,based on deep learning techniques, improve predictive accuracy and real-time event correlation from heterogeneous sources in industrial contexts? To answer this question, the paper proposes an innovative approach for designing and developing an intelligent early warning system. Specifically, it presents the implementation and validation of a predictive model based on Long Short-Term Memory (LSTM) neural networks, trained and tested on real-world industrial data. The system is designed to accurately predict the most frequent error classes, thereby enhancing equipment reliability and improving the planning of maintenance activities. The activities described in this study are part of the SCREAM research project, which aims to develop a platform capable of providing companies with strategic insights into their production processes. The goal is to address both product development dynamics and production-related elements, such as infrastructure and machinery, that are critical for optimising every stage of the supply chain. To achieve this, data from industrial machinery were analysed, with a specific focus on performance metrics and malfunction events. The analysis focuses on the prediction of high-frequency error classes to improve overall system reliability and enable more effective preventive maintenance strategies. The rest of the paper is organised as follows. Section 2 presents the theoretical field on which this study is based. Section 3 explains the research method adopted. Section 4 contextualises the case study and the results reached. Section 5 discusses the results found, including the limitations of the research, implications and potential future research. Finally, Section 6 draws the conclusions, summarising the approach.