## 5 Discussion and Future Research Directions
### Data Sources and Their Integration  
Data sources in predictive maintenance systems encompass a wide range of inputs that enable comprehensive monitoring and analysis of industrial assets. Real-time data are collected from sensors measuring vibration, acoustic signals, temperature, current, pressure, and humidity, often enhanced by IoT-enabled devices for continuous tracking. Historical records--such as maintenance logs, failure histories, equipment usage profiles, and long-term environmental monitoring--provide crucial context for analytics. In parallel, intermediate systems like SCADA, PLCs, and MES support real-time control and decision-making, while external platforms--including cloud services, third-party providers, and vendor-specific tools--offer advanced diagnostics and analytical capabilities. Collectively, these data layers form a robust foundation for the deployment of predictive maintenance strategies (see Figure 10).  
Figure 10: Illustration of data sources for predictive maintenance, highlighting real-time sensors, historical data, intermediate systems, and external platforms.  
Figure 11: Overview of signal processing and data preparation techniques, including transformations, feature extraction, data cleaning, and augmentation.  
Figure 12: Overview of machine learning and advanced analytics in predictive maintenance, highlighting core techniques, specialized models, optimization methods, and explainability tools.  
Despite these advances, multiple surveys and field studies indicate that industrial datasets often lack sufficient breadth or detailed labelling, especially for rare failure events. Consequently, many models become overfit to well-documented faults or underperform due to incompleteness of the input. To mitigate these limitations, researchers are exploring strategies such as data augmentation, synthetic data generation through the use of digital twins, and federated learning. Nonetheless, further investigation is needed to address challenges related to data privacy, cross-organizational collaboration, and the operational constraints of sectors where intellectual property is tightly protected. To move from raw, fragmented logs toward usable data assets, a systematic approach to data curation and publication is required. One such approach is described below.  
From Raw Logs to a Shareable Data Mining Repository  
A critical step in addressing the aforementioned challenges involves structuring the raw data into interoperable, analysis-ready formats. In practice, converting heterogeneous mining logs into a research-ready database involves four recurring steps.  
_(i) Ingest:_ Time-stamped vibration, current, thermographic, and operational tags are streamed into a staging area via OPC UA or MQTT brokers.  
_(ii) Standardise:_ All records are mapped to a common time base (e.g., 1 kHz for fast channels and 1 Hz for SCADA tags) and stored in a column-oriented time-series table, while contextual information--equipment ID, duty cycle, ore hardness, and am  
Figure 13: Summary of key challenges in AI/ML-based predictive maintenance, including data limitations, concept drift, computational costs, explainability, IoT integration, and scalability.  
bient conditions--is preserved in a metadata table compliant with ISA-95 or the Asset Administration Shell.  
_(iii) Label:_ Maintenance tickets, inspection notes, and failure codes are linked to their signal intervals through a simple event table containing {start_time, end_time, fault_type, severity}, making supervised learning possible, even when faults are rare.  
_(iv) Publish:_ The three tables= (\"signals\", \"metadata\", and \"events\") are exported as Apache Parquet or Feather files and a JSON schema so that external researchers can query the whole asset history with a single SQL-like view.  
A well-structured repository of this kind acts as an enabler for scalable and reproducible AI applications.