## 5 Discussion
### Anomaly Methods and Comparative  
The adoption of machine learning methods for anomaly detection in mining infrastructures has demonstrated tangible benefits in predictive maintenance strategies. Nevertheless, certain overarching limitations remain evident. First, data dependency becomes a crucial bottleneck: models rely heavily on large, high-quality datasets for training and validation [2; 6; 63]. Because many mining plants have heterogeneous sensor setups and uneven data acquisition routines, achieving robust training sets poses a real challenge [5; 49]. In addition, maintaining data integrity in dusty, high-vibration environments introduces further complexities in data cleaning and preprocessing [5; 11]. The risk of model under-performance grows especially when sensors degrade or readings become corrupted, thus aggravating the deficiency of reliable ground-truth labels in real conditions [2; 49]. Computational requirements further complicate real-time deployment. Many advanced deep learning architectures require substantial on-board processing power, yet harsh mining sites often have only limited edge-computing capabilities [6; 21]. Although lower-complexity approaches (e.g., random forest, SVMs) might reduce latency, they sometimes struggle to capture nuanced patterns in variable load conditions and multiple fault types [4; 63]. Indeed, any cloud-based solution demands robust connectivity, which is seldom guaranteed in remote and subterranean mines [2; 49]. Consequently, balancing model complexity and hardware constraints remains critical, calling for model compression or lightweight architectures. A key topic to address is interpretability. Many deep or ensemble algorithms behave like \"black boxes\", making it difficult for operators to trust (and thus adopt) a system's diagnosis [5; 43]. In critical operations such as conveyor belts, drills, or ventilation machinery, domain experts require interpretable feedback (e.g., root-cause explanation of predicted wear patterns) [2]. While certain post hoc methods (e.g., SHAP values or rule-based explanations) have been proposed, their application in real-time mining analytics remains in an early stage [49]. Moreover, these model deficits are magnified by the variability of mine sites. Different geology, equipment age, or local climate drastically alter the signal patterns measured by IoT devices [6; 11]. In open-pit operations, for instance, rainfall can temporarily reduce mechanical friction in moving parts; in underground sites, elevated humidity can degrade sensors more rapidly [2; 5]. This heterogeneity implies that predictive models often fail to generalize across multiple sites unless they incorporate continuous learning modules or advanced domain adaptation [2; 43]. From a usability perspective, practical field implementation necessitates additional considerations of cost and integration. Equipment retrofitting with advanced sensors typically involves extra hardware and specialized data-processing pipelines [49]. The overhead costs (installation, training personnel, periodic calibration) can be prohibitive for smaller mines, making extensive adoption challenging [2, 21]. In terms of system integration, bridging these AI-driven solutions with existing Supervisory Control and Data Acquisition (SCADA) or Enterprise Resource Planning (ERP) systems requires a robust data-management architecture [40, 63]. Maintenance staff's skill level also factors in--if the on-site team lacks familiarity with advanced analytics or if the system demands frequent manual recalibration, sustaining reliable operation in harsh or remote conditions can be problematic [5, 49]. Despite these challenges, successful real-world examples offer encouraging evidence. Some open-pit and underground mines have adopted radio-frequency identification (RFID) and sensor fusion to detect belt tears and misalignment in real time [2, 11], while others integrated drill-bit wear prediction tools for autonomous rigs, reducing unplanned downtime [6].