AUTHOR=Dhungana Hariom 

TITLE=Biomimicry as a decision-making methodology in condition monitoring

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=Volume 8 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1485489

DOI=10.3389/frai.2025.1485489

ISSN=2624-8212

ABSTRACT=In maintenance engineering, effective decision-making is critical to ensuring system reliability and operational efficiency. Modern industrial systems are monitored by a multitude of sensors that generate large volumes of data. However, traditional condition monitoring techniques face several limitations: they rely heavily on high-quality, continuous sensor input, struggle with adaptability to new fault scenarios, require significant computational resources, and often provide limited decision support beyond fault detection. These constraints hinder their practical utility in dynamic and resource-constrained environments. This paper introduces a biomimetics-inspired framework for condition management, drawing on principles observed in natural systems to overcome the aforementioned challenges. Biomimetics, an emerging interdisciplinary field, has shown significant promise in bridging gaps between theoretical innovation and practical industrial application. However, its potential remains underutilized in maintenance decision-making systems. In response, our study proposes a biologically inspired methodology that parallels the human cognitive system, integrating multi-sensory data, adaptive learning, and energy-efficient sensing mechanisms to enhance fault diagnosis and decision-making. The core contributions of this research are fourfold: (1) adaptive intelligence through continuous learning that revises rules and cases over time; (2) multi-sensory integration, inspired by animal sensory systems, to improve diagnostic accuracy; (3) data augmentation techniques that address issues of incomplete or noisy input; and (4) the introduction of energy-efficient sensors and biomimetic optimization strategies suitable for IoT and edge devices. To demonstrate the practical applicability of our approach, we conducted empirical studies using vibration data for procedural analytics, validating the framework's effectiveness in real-world fault diagnosis. It serves as a functional roadmap, inviting broader discussion on the integration of biomimetics in maintenance engineering.