Hybrid Neuro-Fuzzy Approach for Transparent Anomaly Detection in Mining Equipment

Predictive maintenance is critical for minimizing downtime and operational costs in mining industries, where slurry pumps operate under abrasive and highly variable conditions. Traditional machine learning models, while accurate, often lack interpretability, limiting their adoption in safety-critical environments. This paper presents an Adaptive Neuro-Fuzzy Inference System (ANFIS) based approach for anomaly detection in slurry pumps using vibration-based features such as displacement, velocity, crest factor, kurtosis, skewness, peak, and peak-to-peak values. ANFIS combines the transparency of fuzzy logic with the learning capability of neural networks, enabling interpretable IF–THEN rules and adaptive tuning of membership functions. A dataset of 33,104 vibration samples was analyzed under four balancing strategies to address class imbalance. Models were evaluated using accuracy, precision, recall, F1-score, RMSE, and clustering quality metrics (Dunn Index and Silhouette Score). The results demonstrate that ANFIS achieves high accuracy (>99%) and strong interpretability, outperforming traditional black-box models. The proposed approach enhances trustworthiness and adaptability in predictive maintenance systems, offering an explainable solution for Industry 4.0 applications. Future work will explore hybrid models, real-time IoT integration, and edge deployment for dynamic operational environments.