## 2 Key Components in AI-Based Predictive Maintenance  
AI-based PdM can be fundamentally broken down into six distinct components: data preprocessing, AI algorithms, decision-making modules, communication and integration, and user interface and reporting, as shown in Figure 3. This section briefly discusses each component to understand how they work together to enable AI-based PdM.  
Figure 3: Key components of an AI-based PdM System.  
1. Sensors: Sensors are the frontline data collectors in a PdM system. These specialized devices are strategically placed on equipment and machinery to continuously monitor various parameters, such as temperature, pressure, vibration, and more. Sensor data provides real-time insights into equipment health and forms the foundation for predictive maintenance analysis.
2. Data Preprocessing: Raw data obtained from sensors often contains noise and inconsistencies. Data preprocessing is the initial step in preparing the data for analysis. It includes data cleaning, normalization, and missing data handling. High-quality data are essential for accurate PdM modeling.
3. AI Algorithms: AI algorithms, including machine learning and deep learning techniques, are the brain of the PdM systems. The algorithms analyze the data to identify the most important features relating to possible failures. They learn from historical data to predict equipment failures, anomalies, and RUL.
4. Decision-Making Modules: The insights and predictions generated by the AI algorithms are processed by decision-making modules. These modules are responsible for determining when maintenance actions are needed. They can recommend preventive or corrective maintenance tasks, schedule maintenance, and trigger alerts to maintenance teams when necessary.
5. Communication and Integration: Communication and integration ensure that the insights generated by the system are effectively translated into action. This component involves interactions with various stakeholders, including maintenance personnel and management. Furthermore, integration with enterprise systems such as ERP and asset management software aligns predictive maintenance with broader organizational goals.
6. User Interface and Reporting: To make these insights accessible to maintenance staff and decision makers, user interfaces and reporting tools are essential. The tools make it easier for users to understand complex data patterns and make informed decisions by providing data visualization, dashboard, and reporting capabilities. Data visualization tools and dashboards communicate data insights and forecast information to maintenance teams and decision makers. Visual aids help understand complex data patterns and make informed decisions.  
The following three data-related units are added to the advanced AI methods to obtain resilient, reliable, secure, and highly stable results using AI-based PdM in complex and dynamic environments.  
Sensor data and the Internet of Things (IoT) integration: Integrating IoT and sensor technology is pivotal in next-step autonomy for PdM tasks. The IoT sensors are strategically placed in equipment and machines to monitor their condition in real-time continuously.  
Data integration: Data integration combines data from various sources, including historical maintenance records, real-time sensor data, external factors (e.g., weather), and production schedules. This holistic view of equipment health enhances decision making.  
Digital twins: Digital twins create virtual replicas of physical assets, facilitating real-time simulation and monitoring. AI systems monitor these digital twins, identifying performance irregularities and recommending optimal maintenance strategies before any physical equipment is adversely affected.  
Edge and cloud computing: Edge computing proceeds closer to the data source through IoT sensors for real-time analysis rather than in a centralized data center, which reduces latency and enables real-time analysis. Cloud computing stores and manages enormous amounts of data to analyze historical events.