Transparent and explainable AI for fault prediction in distribution networks

Reliability of distribution networks is paramount, especially in industrial settings with major operational and financial stakes. We present a sensor-less fault prediction solutionIntelliView, leveraging existing current and voltage recordings—eliminating costly hardware—and achieving over 95% precision in diverse real-world deployments. The system consistently flags warning signs of impending faults by integrating robust feature processing, advanced anomaly filtering, and refined AI components (including specialized LSTM variants and attention mechanisms). Notably, it predicts two out of three incipient events up to one week in advance, allowing operators to intervene before anomalies escalate into significant failures. A key strength of IntelliView is its transparent, Explainable AI (XAI) framework, enabling users to understand the rationale behind each alarm. This fosters trust and streamlines decision-making in high-stakes environments. Beyond basic fault detection, the solution offers detailed insights into root causes, fault location, and potential time-to-failure—enabling proactive maintenance and reducing false alarms. Designed for flexible deployment, it adapts seamlessly to on-premises, cloud-based, or hybrid infrastructures, ensuring broad compatibility. By uniting data-driven anomaly detection with engineering rigor, this approach paves the way for more resilient, intelligent distribution grids and new standards in predictive maintenance.