Next-Generation AI Architecture for Real-Time Hazard Prediction and Safety Optimization in Mining Systems

AIOT-Based Predictive Safety Framework for Underground Coal Mining: Integrating PINNs, Wearable Sensors, and Digital Twins in Indian Contexts

Underground coal mining remains one of the most hazardous industrial activities worldwide, particularly in emerging economies where complex geological conditions, methane emissions, roof instability, dust exposure, and equipment-related accidents continue to threaten worker safety. India, the world's second-largest coal producer, operates numerous underground mines under challenging geotechnical and environmental conditions. Despite significant advancements in mechanization and monitoring technologies, accident investigations indicate that a substantial proportion of mining incidents remain attributable to delayed hazard detection, fragmented monitoring systems, and limited predictive capabilities. Conventional safety management approaches are largely reactive, relying on threshold-based alarms and post-event analysis rather than proactive risk prediction. This study proposes a Mining 5.0-oriented intelligent safety framework that integrates Artificial Intelligence of Things (AIoT), Physics-Informed Neural Networks (PINNs), wearable sensing technologies, and Digital Twin models for real-time hazard prediction and decision support in underground coal mines. The proposed framework combines data from methane sensors, temperature sensors, air velocity monitors, geotechnical instruments, equipment health monitoring systems, and wearable devices measuring worker location, physiological status, and environmental exposure. These heterogeneous data streams are fused within a Digital Twin environment that continuously replicates underground mine conditions. PINNs are employed to incorporate ventilation physics, methane transport dynamics, and geomechanical principles into machine-learning models, thereby improving prediction accuracy and interpretability under sparse or uncertain data conditions. The study identifies critical research gaps in existing mine safety systems, including inadequate integration of physical laws with AI models, limited utilization of worker-centric sensing technologies, and the absence of comprehensive Digital Twin platforms for proactive safety management. To address these gaps, a socio-technical framework is developed that enables continuous risk assessment, predictive analytics, and human-AI collaborative decision-making. The proposed approach is expected to enhance situational awareness, reduce accident probability, improve emergency preparedness, and support sustainable Mining 5.0 transformation in India.

The role of Artificial Intelligence in improving construction site safety

Artificial intelligence, when responsibly implemented, represents a transformative adjunct to traditional safety practices – capable of significantly improving construction site safety performance globally – but it must be deployed in tandem with organizational commitment, worker training, and robust safety cultures.

Artificial Intelligence Model for Failure Prediction and Safety Enhancement in Manufacturing Plants

Ensuring reliable equipment operations are critical for production efficiency and safety compliance in manufacturing industries. Unexpected machine breakdowns not only disrupt operations but also increase maintenance costs and safety risk. Traditional approaches — whether reactive or preventive — often fail to incorporate real-time equipment data and often overlook early fault indicators. Predictive maintenance strategies based on artificial intelligence models, address these shortcomings by monitoring machine conditions in real time, detect anomalies and forecast failures. Thus, this study proposes a refined deep neural network to classify machine health and estimate failure probability, thereby emphasizing Remaining Useful Life (RUL) prediction as a strategy for optimizing maintenance scheduling. In this study, the AI4I 2020 Predictive Maintenance dataset, which contains 10,000 records of machine operating conditions, including air and process temperatures, rotational speed, torque, tool wear, product type, and failure status was used for the evaluation of the proposed model. To improve the proposed model's performance, detailed preprocessing steps were deployed on the dataset. These steps include some preliminary categorical encoding and feature standardization on the dataset while class weighting, SMOTE and focal loss were deployed for handling class imbalance issues. According to the results achieved, the proposed model performed better across all metrics considered in comparison with similar models including baseline models. By integrating data-driven AI techniques with predictive maintenance strategies, this research study demonstrates how manufacturing plants can reduce downtime, extend machine lifespan, and minimize unnecessary maintenance interventions.

AI-based Analytical System for Anomaly Detection in Industrial Processes

This study develops and evaluates an AI-based analytical system for detecting anomalies in industrial processes. The work reviews major sources of risk in industrial control systems, distinguishes point, contextual, and collective anomalies, and summarizes the principal machine-learning approaches used for industrial anomaly detection. A synthetic dataset modeled on the Secure Water Treatment (SWaT) testbed was created with 10 sensor and actuator variables and 10,000 one-second observations, including 1,000 anomalous samples. After missing-value interpolation, duplicate removal, low-variance filtering, and standardization for consistent analysis and visualization, an Isolation Forest with 200 trees was trained in a novelty-detection configuration using normal operating data. On the held-out test set, the model achieved 88.63% accuracy, 45.86% precision, 75.67% recall, and an F1-score of 57.11%. The results show that Isolation Forest can detect most simulated anomalies, although the relatively low precision indicates a substantial false-alarm burden. Future work should validate the approach on authorized real SWaT or PLC-SCADA data, investigate hybrid temporal models, and incorporate explainable-AI methods to support operator decision-making.

AI-Enabled Digital Twin Framework for Real-Time Structural Health Monitoring and Damage Prediction in Smart Civil Infrastructure

Enhancing predictive safety monitoring in longwall mining via interpretable multi-scale data fusion

The predictive performance of safety monitoring systems in longwall mining faces is frequently compromised by complex, non-linear environmental noise and dynamic extraction processes. While conventional time-series decomposition techniques can mitigate sensor noise to extract salient temporal patterns, they frequently underperform in capturing the complex multivariate interactions among distributed sensors, thereby limiting the effectiveness of the monitoring system. To alleviate this performance bottleneck, this study proposes an integrated forecasting framework that combines multi-scale data decomposition with a self-attention-based fusion mechanism. This hybrid approach dynamically synthesises diverse temporal scales and multivariate feature interactions, contributing to the system’s resilience against volatile data fluctuations. Rigorous evaluations using a canonical public dataset from the Upper Silesian coal basin reveal that, across a comprehensive suite of architectures (including Encoder-Decoder LSTM/GRU, standard Transformer, Informer, and Autoformer), models trained on the fused decomposed features generally exhibit enhanced forecasting robustness and sustained predictive stability across most temporal configurations. Furthermore, SHapley Additive exPlanations (SHAP) are integrated to provide feature-level transparency. The interpretability analysis demonstrates that the self-attention mechanism effectively assigns higher weights to underemphasised, distant sensor inputs critical for robust forecasting, compensating for the limitations of traditional decomposition methods in capturing complex multivariate dependencies. By facilitating both high predictive fidelity and transparent reasoning, this framework offers a robust analytical foundation for proactive hazard mitigation and advanced safety monitoring in underground mining.