Harnessing AI forward and backward chaining with telemetry data for enhanced diagnostics and prognostics of smart devices

In the rapidly evolving landscape of artificial intelligence (AI) and the Internet of Things (IoT), the significance of device diagnostics and prognostics is paramount for guaranteeing the dependable operation and upkeep of intricate systems. The capacity to precisely diagnose and preemptively predict potential failures holds the potential to considerably amplify maintenance efficiency, diminish downtime, and optimize resource allocation. The wealth of information offered by telemetry data gathered from IoT devices presents an opportunity for diagnostics and prognostics applications. However, extracting valuable insights and making well-timed decisions from this extensive data reservoir remains a formidable challenge. This study proposes a novel AI-driven framework that integrates forward chaining and backward chaining algorithms to analyze telemetry data from IoT devices. The proposed methodology utilizes rule-based inference to detect real-time anomalies and predict potential future failures, providing a dual-layered approach for diagnostics and prognostics. The results show that the diagnostics engine using forward chaining detects real-time issues like "High Temperature" and "Low Pressure," while the prognostics engine with backward chaining predicts potential future occurrences of these issues, enabling proactive prevention measures. The experimental results demonstrate that adopting this approach could offer valuable assistance to authorities and stakeholders. Accurate early diagnosis and prediction of potential failures have the capability to greatly improve maintenance efficiency, minimize downtime, and optimize cost.