Machine Fault Diagnosis Techniques

Neueste Paper

• When Does Domain Adaptation Help on Physical Vibration Sensors? A Held-Out-Bearing Study of Neural-Operator and Convolutional Models

  Kumbha Nagaswetha, Rabi Pathak · 29. September 2026

  Diagnosing rolling-element bearing faults from vibration is a canonical physical-sensing task and a widely used benchmark for domain adaptation under operating-condition shift. Accuracies above 99 percent are commonly reported, but under evaluation splits that place the same physical bearing in both…

• Scalable and Data-Driven Decision Support in the Maintenance, Repair, and Overhaul Process

  Houkun Zhu, Helena Ebel, Dominik Scheinert, Florian Schmidt, Jens Altenkirch, Odej Kao · 29. September 2026

  Several businesses apply maintenance, repair, and overhaul (MRO) principles to the life-cycle of their existing products. In cases like casted gas turbine component Product Lifecycle Management (PLM), repairing components in frequent intervals can extend the lifetime expectation of the product, prov…

• Factorized axis convolutional gated recurrent unit with dynamic adaptive pooling for remaining useful life prediction of rolling bearings

  Hanbyeol Park, Jungho Choo, Hyerim Bae · 28. September 2026

  Convolutional neural networks (CNN) are widely used to predict the remaining useful life (RUL) of rolling bearings from time-frequency representations (TFRs) of vibration signals. However, during degradation, characteristic structures in TFRs align predominantly along the frequency or time axis, mak…

• When Labels Are Scarce: An Oscillatory State Space Model for Vibration Diagnosis

  Mainak Mallick, Seung-Kyum Choi · 24. September 2026

  Machine fault diagnosis from vibration requires learning from scarce labelled fault recordings while meeting the computational constraints of edge devices for local inference. We introduce DualRes, a compact oscillatory state-space model that combines two complementary spectral views of vibration, c…

• Multi-Term Fourier Graph Neural Network with Sample Relationship Learning for Enhanced Remaining Useful Life Prediction

  Ya Song, Laurens Bliek, Yaoxin Wu, Yingqian Zhang · 23. September 2026

  Predicting the remaining useful life (RUL) is essential for effective predictive maintenance. Spatio-Temporal Graph Neural Networks (ST-GNNs), which can model both temporal and spatial relationships by representing time series data as a sequence of graphs, have shown exceptional performance in RUL p…

• Stagewise Anomaly Detection for E-Transaxle Quality Monitoring Using Wavelet and STFT Features

  Mohammad N. Bisheh, Rajesh Gupta, Qian Wang, Mohammad Babakmehr, Colin Brady, Parinaz Farajiparvar, Saurabh Singh, Kamran Payanabar · 22. September 2026

  This paper presents two interpretable machine-learning frameworks for quality screening of e-transaxle assemblies in electric vehicles: a Stagewise Wavelet Isolation Forest (SWIF) framework and a short-time Fourier transform (STFT)-based diagnostic framework. High-dimensional vibration signals acqui…

• Explainable Predictive Condition-based Maintenance of Naval-Propulsion Systems using Fuzzy Logic

  Dionisis Kalogeropoulos, Georgia Sovatzidi, Panagiotis G. Kalozoumis, Dimitris K. Iakovidis · 22. September 2026

  The shipping industry has a significant impact on the global economy, emphasizing the need for operational availability and safety through the use of effective maintenance techniques. During the last decades, predictive maintenance (PdM) has emerged as a promising solution compared to the existing c…

• Benchmarking Hybrid Deep Learning Architectures for Predictive Maintenance in Industry 4.0

  Zhengyang (Cissy), Gu, Joseph E. Hernandez, Thomas Cook, John Burtenshaw, Sean Scott, Chris Couch · 22. September 2026

  Predictive maintenance in Industry 4.0 refers to using data from sensors, machines, and production systems to estimate when equipment is likely to fail, so maintenance can be planned before a breakdown occurs [1]. However, a model that predicts maintenance may work perfectly in the lab but fail unex…

• Contrastive Siamese Representation Learning for Predictive Maintenance of Electrical Submersible Pumps

  Seshu K. Damarla, Xiuli Zhu · 22. September 2026

  Electrical submersible pumps (ESPs) are essential in offshore oil production, where unexpected failures can result in significant operational and financial losses. Accurate predictive maintenance for ESP systems remains challenging due to nonlinear operating conditions, class imbalance, and variabil…

• Uncertainty and Business-Aware Remaining Useful Life Estimation for Semiconductor Manufacturing

  Davide Frizzo, Francesco Borsatti, Gian Antonio Susto · 22. September 2026

  Semiconductor manufacturing relies on tightly interconnected components, so early identification of the assets most likely to fail is essential to prevent a single breakdown from disrupting the entire production pipeline. Maintenance planning must therefore balance unexpected failures against premat…

• FreqCondNorm: Towards Cross-domain Predictive Maintenance through a Frequency-Conditioned Transformer Foundation Model

  Zaynab Raounak, Camille LHermine, Zhiguo Zeng · 18. September 2026

  Deep learning predictive maintenance models suffer from poor transferability across machines and operating conditions, especially when labelled data are scarce and signals span five orders of magnitude in sampling frequency (1 Hz to ~100 kHz). We propose FreqCondNorm, a Transformer-based architectur…

• Robust Prototypical Networks for Few-Shot Sensor Fault Diagnosis

  Mohammed Ayalew Belay, Amirshayan Haghipour, Pierluigi Salvo Rossi · 14. September 2026

  Industrial fault diagnosis often operates with only a handful of labeled fault examples, making few-shot learning attractive for sensor monitoring. Standard prototypical networks are simple and effective; however, their class prototypes may become unstable in the very-low-shot regime because each de…

• Deep Learning-Based Detection of Electrical Faults and Power Quality Disturbances in Aerospace Power Systems

  Ian C. Guzm\'an, Radu Babiceanu, Berker Pek\"oz · 10. September 2026

  More Electric Aircraft require fast and reliable monitoring of high-frequency electrical networks, yet most power quality disturbance and fault diagnosis methods are developed for conventional 50 or 60 Hz grids. This work presents a hardware-aware deep learning framework for multiclass detection of …

• Quantile-Led Feature Extraction for Multi-Horizon Predictive Maintenance in Industrial Manufacturing Systems

  David J Poland, Daniele Ravi, Na Helian · 9. September 2026

  In data-driven predictive maintenance (PdM), feature extraction is usually treated as fixed preprocessing: a descriptor set is chosen once and reused while the downstream model or forecasting horizon changes. This paper isolates the representation-learning stage and presents a quantile-led feature-e…

• Long Horizon Transformer Quantile Fault Prediction for Multi Site Industrial Predictive Maintenance

  David J Poland, Daniele Ravi, Na Helian · 7. September 2026

  Long-horizon predictive maintenance requires models to distinguish slowly evolving degradation from normal operating-regime variation over planning windows measured in days rather than hours. This paper evaluates whether an explicit conditional-quantile representation provides an informative classif…

• A Deep Learning-Based Stacking Ensemble Framework for Turbofan Engine Remaining Useful Life Prediction

  Limon Bin Hossain, Md. Salehin Seyam, Md Rashedul Islam, Abdur Rahman, Md Sharifuzzaman · 31. August 2026

  This study proposes a two-level stacking ensemble framework for Remaining Useful Life (RUL) prediction of turbofan engines, evaluated on the NASA C-MAPSS benchmark using the FD001 and FD003 subsets. The framework integrates four heterogeneous deep learning base learners: Long Short-Term Memory (LSTM…

• Time-Series Retrieval for Grounding Multimodal Language Models in Remaining Useful Life

  Valeriu Dimidov, Rapha\"el Frank · 21. August 2026

  Large language models (LLMs) and agentic AI systems are increasingly being explored for domain-specific maintenance and prognostics tasks, raising the question of whether they can effectively support prognostics and health management (PHM). In this paper, we investigate remaining useful life (RUL) e…

• Multi-Class Electrical and Mechanical Fault Classification Using Random Convolutional Kernels

  Mouhamadou Mansour Lo, Mouad Talbaoui, Gildas Morvan, Mathieu Rossi, Fabrice Morganti, David Mercier · 20. August 2026

  Diagnosing faults in rotating machinery is essential for ensuring the reliability of industrial processes. Random convolutional kernel-based Time Series Classification (TSC) methods, such as ROCKET and its variants, provide an attractive trade-off between predictive performance and computational eff…

• A Metamorphic Artificial Age Score Decision-Support Prototype for Flight-Log-Based Drone Propeller Health Monitoring

  Seyma Yaman Kayadibi · 20. August 2026

  Drone propeller faults can create safety and reliability risks when their effects are distributed across multiple flight-log channels rather than appearing as a single diagnostic signal. This paper proposes a Metamorphic Artificial Age Score (AAS) decision-support prototype for flight-log-based dron…

• Cross-Domain Industrial Fault Detection by Causal Mechanism Monitoring

  Dhiraj Neupane, Mohamed Reda Bouadjenek, Richard Dazeley, Sunil Aryal · 18. August 2026

  Unsupervised fault detection in industrial systems is dominated by reconstruction based methods that monitor individual sensor marginal distributions. This misses coupling faults, where the physical relationship between sensor groups breaks while marginal statistics remain normal. Such faults evade …

• Physics-Informed Machine Learning in Prognostics and Health Management: A Systematic Literature Review

  Christopher Braun, Julian Raible, Marco F. Huber · 12. August 2026

  In modern industry, keeping complex systems reliable, safe, and efficient hinges on Prognostics and Health Management (PHM). Machine Learning (ML) has largely driven advancements in diagnostics and prognostics, yet purely data-driven models face inherent limitations, such as poor generalization, an …

• Failure-Mechanism Transferability of Cumulative-Damage Features for Health State Estimation of SiC Power Modules

  Mattia Scarpa, Evgeny Kusmenko, Francesco Toso, Mattia Bruschetta, Ruggero Carli, Simon Achatz · 11. August 2026

  Data-driven health-state estimators for SiC (Silica-Carbide) power modules typically report their performance on a single accelerated-aging campaign, and how that performance transfers to a different failure mechanism is rarely tested. We benchmark five reference methods from the prognostics and con…

• Physics-Informed Condition Monitoring of SiC Power Modules

  Mattia Scarpa, Evgeny Kusmenko, Francesco Toso, Mattia Bruschetta, Ruggero Carli, Simon Achatz · 11. August 2026

  Silicon carbide (SiC) power modules are increasingly deployed in automotive traction inverters, where condition monitoring is essential to prevent in-service failures. Despite extensive qualification under AQG 324, no consolidated approach exists for in-field health state estimation: physics-of-fail…

• A Time-Frequency Dual-Domain Multi-Scale Convolutional Neural Network for Bearing Fault Diagnosis under Strong Noise

  Yanxi Ding, Tingyue Jia · 11. August 2026

  To address the degradation of bearing fault diagnosis accuracy under strong noise, this paper proposes a time-frequency dual-domain multi-scale convolutional neural network. The time-domain branch employs three parallel convolutional kernels to capture multi-scale impulse features, while the frequen…

• Machine-Learning-Based Diagnostic Framework for Passive Ultrasonic Detection of Railway Wheel Defects

  Aashish Shaju, Steve Southward, Mehdi Ahmadian · 11. August 2026

  Reliable identification of railway wheel defects is important for safety and maintenance. This study develops a machine-learning-based diagnostic framework for multi-class defect identification using passive air-coupled ultrasonic acoustic emission signals. Data were collected from eleven full-scale…