International Journal of Prognostics and Health Management

A Novel Taxonomy and Approaches for the Identification of Frequently Occurring Regularities in Degradation Processes of Engineering Systems

2025-10-28T11:36:26+00:00

The trend is shifting toward hybrid methods that incorporate prior knowledge into data-driven methods to address challenges in diagnostics and prognostics such as limited data, interpretability, and complex system behavior. While system-specific prior knowledge facilitates accurate, physically plausible modeling, the resulting hybrid model is typically tightly coupled to an individual engineering system. In contrast, general prior knowledge—such as fundamental physical laws or broadly applicable degradation knowledge—supports scalable, transferable models across various engineering systems. This opens the door to more adaptable approaches for diagnostics and prognostics, but the potential remains underexplored. To address this, a taxonomy is proposed that defines prior knowledge as frequently occurring regularities with four levels of validity, enabling hybrid methods to be characterized by their expected transferability. Two approaches are introduced and applied, both aimed at systematically identifying such regularities: one driven by expert knowledge, the other by data. Expert interviews further validate both the taxonomy and the identified regularities, establishing a foundation for developing transferable hybrid methods between various engineering systems.

Artificial Intelligence Technologies for Aircraft Maintenance

2025-11-04T19:20:44+00:00

Effective aircraft maintenance is crucial in ensuring safety, reliability, and cost-effectiveness in the aviation industry. Recent research and industry developments in artificial intelligence (AI) raise the potential to transform various aspects of aircraft maintenance, including predictive maintenance, fault diagnosis, and aircraft health monitoring and management. This paper presents a systematic literature review of AI technologies such as Automated Reasoning and Deep Learning in aircraft maintenance, highlighting its challenges and prospects. An extensive literature search resulted in a final dataset of 696 publications, covering the 40-years period from 1984 till 2024 and describing AI applications in airworthiness management, aircraft health monitoring, and maintenance, repair, and overhaul operations. These publications were analyzed to identify key AI technologies and related aircraft maintenance processes, identifying trends, popular research venues, and underexplored areas. The review concludes with insights into AI adoption in aircraft maintenance and its potential implications for researchers, practitioners, educators, and other stakeholders.

Fault-Tolerant Control for Four-Wheels Independently Actuated Electric Vehicles

2025-12-12T07:22:24+00:00

This paper presents a novel Active Fault-Tolerant Control (AFTC) framework for a four-wheel drive (4WD) electric vehicle equipped with independently actuated in-wheel motors (IWMs). The presented approach consists of a fault detection and diagnosis (FDD) module and a compensation strategy. Once a fault is detected, the FDD module is activated, and as a consequence the fault will be identified, the faulty wheel will be isolated, and fault magnitude will be estimated. Then, based on the FDD module outputs, compensation module strategy is initiated. Compensation module employs a multi-parametric optimization technique to achieve the main objective of reducing the torque demand to the faulty actuator. Through extensive MATLAB/Simulink simulations, the results of this study showcase the effectiveness of the proposed AFTC system in managing multiplicative faults affecting the IWMs of the electric vehicle.