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

Farooq, Muhammad Shoaib and Mir, Rizwan Pervez and Alvi, Atif and Tutusaus, Kilian and García Villena, Eduardo and Alrowais, Fadwa and Karamti, Hanen and Ashraf, Imran UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, kilian.tutusaus@uneatlantico.es, eduardo.garcia@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED (2025) Harnessing AI forward and backward chaining with telemetry data for enhanced diagnostics and prognostics of smart devices. Scientific Reports, 15 (1). ISSN 2045-2322

Text s41598-025-89266-9.pdf Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (5MB)

Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Internet of Things; Telemetry data; Diagnostics and prognostics; Forward chaining; Reverse chaining
Subjects:	Subjects > Engineering
Divisions:	Europe University of Atlantic > Research > Scientific Production Ibero-american International University > Research > Scientific Production Ibero-american International University > Research > Scientific Production Universidad Internacional do Cuanza > Research > Scientific Production University of La Romana > Research > Scientific Production
Depositing User:	Sr Bibliotecario
Date Deposited:	14 Mar 2025 09:16
Last Modified:	14 Mar 2025 09:16
URI:	http://repositorio.funiber.org/id/eprint/17139

Actions (login required)

View Item