Jul 2026· International Scientific Unity· 0 citations
TL;DR
The paradigm shift from testing for correct instruction execution to testing for robust behavior under open‑world conditions is analyzed and practical recommendations for integrating metamorphic testing and formal robustness verification into existing validation pipelines for mPNT systems are concluded.
Abstract
The integration of AI‑based components (deep learning odometry, AI‑driven fault
detection, end‑to‑end localization) into multi‑source Positioning, Navigation and
Timing (mPNT) systems fundamentally changes the nature of testing. Unlike
traditional deterministic algorithms, neural networks operate as “black boxes” trained
on finite datasets and can fail unpredictably on unseen inputs. This paper analyzes the
paradigm shift from testing for correct instruction execution to testing for robust
behavior under open‑world conditions. Three core challenges are examined: the oracle
problem, out‑of‑distribution vulnerability, and the risk of “hallucinations”. A
comparative analysis of testing methods for classical and AI‑based navigation components is provided, followed by a detailed case study of the OdoTest framework
for deep odometry. The paper concludes with practical recommendations for
integrating metamorphic testing and formal robustness verification into existing
validation pipelines for mPNT systems.
An application-focused review of 35 selected empirical studies focusing on the use of AI during software testing, based on PRISMA guidelines, reveals that large language models, machine learning, and computer vision can significantly improve testing efficiency.
Guilherme Martins, Nelson N. Tenório, Jorge Bernardino· Big Data and Cognitive Compu...· 1 citation
The integration of artificial intelligence into instrumentation and measurement systems has emerged as a transformative force across industrial, environmental, and scientific domains. This article provides a systematic overview of AI applications in instrumentation, encompassing intelligent sensor data processing, predictive maintenance, automated meter reading, and fault diagnosis. Drawing upon recent advances documented in the literature, we examine the methodological landscape ranging from conventional machine learning to deep learning and large language models. Key benefits include enhanced measurement accuracy through intelligent compensation, reduced downtime through predictive maintenance, and improved operational efficiency through automation. However, significant challenges persist, including the blackbox nature of AI models, data scarcity, uncertainty quantification, and the gap between laboratory performance and realworld deployment. We argue that the future of intelligent instrumentation lies in hybrid approaches that integrate datadriven AI with conventional modeldriven methods, thereby combining the pattern recognition capabilities of AI with the interpretability and rigor of physicsbased models.
Fujie Lu· Academic Journal of Manageme...· 0 citations
Results show that the proposed approach provides critical AI-based support, enabling inspectors to certify component acceptance or rejection with greater reliability and confidence, and facilitates next-generation NDT 5.0 systems and empowers inspectors in highly automated environments.
J. Mendikute, Jose Luis Lanzagorta, I. Sanchez et al.· e-Journal of Nondestructive...· 0 citations
A data engine which gathers data and improves its performance while executing the task, and demonstrates the ability to learn and reduce the need for expensive verifications over time, while staying within the set error-rate.
Zebin Duan, Norbert Krüger, Juan Heredia et al.· 0 citations
A systematic analysis of the machine learning and deep learning models underpinning vehicle autonomy, spanning classical convolutional neural networks for object detection and semantic segmentation to recurrent and Transformer-based architectures for trajectory prediction and motion planning is presented.
Esraa Khatab, Fares Fathy, Abdallah AlKholy et al.· Machine Learning and Knowled...· 0 citations
To maintain high efficiency and reduce operational downtime in industrial manufacturing, effective Predictive Maintenance (PdM) for robotic manipulators is essential. Although combining Model-Agnostic Meta-Learning (MAML) with digital twin technology offers a solid basis for quickly identifying faults, conventional methods often face challenges regarding parameter sensitivity and generalizing to new domains. To mitigate these issues, we introduce an ensemble-based metalearning framework that combines MAML with majority voting and operational grouping. This methodology improves generalization, stabilizes performance across diverse conditions, and strengthens few-shot learning capabilities. We validated the framework using a synthetic vibration dataset generated via a digital twin to simulate various robotic arm faults. Our findings demonstrate that this method achieves 93.8% accuracy and 93.1% precision in the ten-shot regime, outperforming the MAML baseline by 11.1%, across a broad range of mechanical faults, showing strength in cross-domain few-shot (CDFS) scenarios. Comparisons with established frameworks - including Reptile, Protonet, and ANIL, confirm the effectiveness of our model. By employing ensemble learning, we attain greater robustness and classification accuracy, establishing the method as a viable solution for industrial PdM. Furthermore, the integration of digital twins bridges the gap between simulation and real-world deployment, reducing data dependency and enabling effective fault classification even in dynamic environments with limited labeled data.
Mainak Mallick, Seung-Kyum Choi· 2026 6th International Confe...· 0 citations