# An EWS-LSTM-Based Deep Learning Early Warning System for Industrial Machine Fault Prediction
## 1 Introduction  
The advent of Industry 4.0 [1] was a revolutionary event [2]. The core and innovative element was the use of data to transition from traditional programmed and control-based processes and systems to intelligent processes and systems that can predict the behaviour of various stakeholders in the industry value chain (e.g., customers, operators, machines, etc.) and proactively adjust their operations at different levels [3]. It aims to maximise production efficiency by achieving self-awareness, self-prediction and self-maintenance [4, 5]. A key point of Industry 4.0 is the interaction between production and maintenance planning [6]. The term maintenance planning \"identifies the set of technical, administrative, and managerial activities carried out during the life cycle of an item, with the aim of maintaining or restoring its functionality\" [7]. In this context, early warning systems (EWSs) have emerged as valuable tools to anticipate unexpected disruptions and support maintenance strategies [8]. Indeed, EWSs are designed to provide timely alerts and notifications about potential risks, hazards, or critical events [9; 10; 11]. These systems employ real-time data from a variety of sources, including sensors, surveillance cameras, and environmental monitors, to identify discrepancies from standard conditions [12]. They employ statistical analysis, machine learning, and pattern recognition algorithms to identify unusual patterns or anomalies that might indicate the occurrence of an undesired event [13; 14]. EWSs are typically designed with predefined thresholds or rules that trigger alerts when specific conditions are met [15]. The benefits of EWSs are considerable [16; 17; 18; 19]. While they are widely used in disaster management and environmental monitoring [11; 20; 21], their versatility has enabled their adoption across multiple domains including manufacturing, healthcare, finance, and business management [11]. In the healthcare sector, for instance, the EWS supports clinical assessment to detect acute deterioration at an early stage in order to prevent or reduce adverse events such as unexpected cardiopulmonary arrests, intensive care unit admissions and deaths [22]. In the manufacturing context, EWSs are used to detect early signs of possible interruptions in the production cycle, caused either by machine downtimes or production errors. Targeted actions on product quality control enable EWSs to optimise both quality and productivity [23]. In the financial sector, EWSs are instrumental in anticipating the onset of financial crises in individual countries [24]. Another research line focusing on EWSs leads to identifying early warning signals for decision-makers. These signals can be derived from statistical data, artificial intelligence (AI), or cognitive behavioural techniques. The EWSs in question are referred to by the term \"managerial early warning systems\" (MEWSs) [25]. Although MEWSs can automate the detection of early warning signals, managers still determine how to respond to them. The purpose of these studies is not automation, but rather helping managers develop strategies [25; 26; 27]. Across all contexts, EWSs today are referred to as intelligent because they employ the technologies inherent to warning systems, such as sensors, and the technologies inherent to artificial intelligence in a synergistic approach; although there is no strict definition of an intelligent early warning system (IEWS) in the literature, there are numerous examples of implementation [28; 29; 30; 31; 32]. The IEWS is a significant advancement in condition-based monitoring systems. It uses big data technology and intelligent algorithms to continuously monitor equipment in various conditions. The system can predict future signal changes, detect faults in the creep process before they occur, diagnose equipment faults, and calculate the equipment's remaining useful life [33].