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

Fabian Mauthe — 2025-12-28

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

Dmitry Pavlyuk — 2025-12-28

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.

A Low-Cost, Scalable Approach for Compressor Fault Monitoring Using Deep Learning on Acoustic Signals

Sumana Roy — 2026-02-22

ABSTRACT

In chemical and process industries, reciprocating air compressors are critical single-line equipment whose unexpected failure can trigger plant-wide shutdowns.Legacy compressors often lack built-in monitoring systems, posing significant challenges for early fault detection.This study proposes a non-intrusive, deep learning-based framework for detecting compressor faults through acoustic signal analysis, aiming to retrofit predictive maintenance capabilities into aging assets.

A publicly available dataset of air compressor acoustic recordings was utilized, encompassing healthy and seven fault conditions.Sequential models based on Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) networks were first developed using manually extracted spectral features.Subsequently, a Convolutional Neural Network (CNN) was trained directly on mel-spectrogram representations of the sound signals.Data augmentation techniques were employed to improve model generalization.
Performance was evaluated through per-class precision, recall, F1-score, confusion matrices, and cross-validation.The LSTM model achieved a validation accuracy of 92%, which improved to 94% with the BiLSTM architecture.The CNN model achieved 96.6% validation accuracy, further increasing to 98.3% after augmentation, with a macro-F1 score of 98.6%.Cross-validation demonstrated stable performance (±0.4% deviation).
A real-world proof-of-concept test on 20 new compressor recordings achieved 95% accuracy, validating the model’s practical deployment capability.The proposed deep learning framework provides a scalable, cost-effective solution for sound-based fault diagnosis in compressors, eliminating the need for physical sensor installations.The CNN model trained on mel-spectrograms proved particularly effective, offering near-real-time prediction performance with minimal hardware requirements.

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

Farah Shalhoub — 2025-12-28

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.

RUL Estimation of Rolling Element Bearings Using a Hybrid Wavelet Packet Decomposition–Recursive Feature Elimination–Adaptive Neuro Fuzzy Inference System Framework

Abdel Wahhab Lourari — 2026-02-25

Rolling element bearings are critical components in rotating machinery, and their unexpected failures can cause severe downtime and economic losses. Therefore, accurate estimation of remaining useful life (RUL) is essential to ensure system reliability and enable predictive maintenance strategies. This paper presents a novel hybrid framework that integrates Wavelet Packet Decomposition (WPD), Recursive Feature Elimination (RFE), and Adaptive Neuro-Fuzzy Inference System (ANFIS) for intelligent RUL estimation of bearings. First, vibration signals from the well-known IMS dataset are acquired and decomposed using WPD to capture multi-resolution information. A comprehensive set of health indicators is then computed from each decomposition level, reflecting the degradation dynamics of bearings. To reduce redundancy and enhance discriminative power, the most relevant features are selected using the RFE algorithm. Finally, the refined features are fed into an ANFIS model to estimate the RUL. Comparative analyses with multiple Artificial Neural Network (ANN) based models are conducted to assess the effectiveness of the
proposed approach. Experimental results demonstrate that the hybrid WPD–RFE–ANFIS framework achieves outstanding predictive performance, reaching an accuracy of 99.98%, thereby outperforming traditional ANN architectures. This study highlights the potential of hybrid intelligent models for advancing prognostics and health management (PHM) in industrial applications.