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   Articles with "machine fault" as a keyword



A review of stochastic resonance in rotating machine fault detection

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recommendations! Published in 2019 at "Mechanical Systems and Signal Processing"

DOI: 10.1016/j.ymssp.2018.06.032

Abstract: Abstract Condition-based monitoring and machine fault detection play important roles in industry as they can ensure safety and reduce breakdown loss. Weak signal detection is an essential stage in many signal processing-based machine fault detection… read more here.

Keywords: rotating machine; machine fault; fault detection; machine ... See more keywords
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Deep Transfer Network With Multi-Kernel Dynamic Distribution Adaptation for Cross-Machine Fault Diagnosis

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recommendations! Published in 2021 at "IEEE Access"

DOI: 10.1109/access.2021.3053075

Abstract: Recently, various deep learning models, which are mainly based on data-driven algorithms, have received more and more attention in the field of intelligent fault diagnosis and prognostics. However, there are two major assumptions accepted by… read more here.

Keywords: diagnosis; machine fault; cross machine; transfer ... See more keywords

Lifeisgood: Learning Invariant Features via In-Label Swapping for Generalizing Out-of-Distribution in Machine Fault Diagnosis

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recommendations! Published in 2025 at "IEEE Transactions on Cybernetics"

DOI: 10.1109/tcyb.2025.3578712

Abstract: In machine fault diagnosis, conventional data-driven models trained by empirical risk minimization (ERM) often fail to generalize across domains with distinct data distributions caused by various machine operating conditions. One major reason is that ERM… read more here.

Keywords: machine fault; learning invariant; fault diagnosis; lifeisgood ... See more keywords

Self-Training Reinforced Adversarial Adaptation for Machine Fault Diagnosis

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recommendations! Published in 2023 at "IEEE Transactions on Industrial Electronics"

DOI: 10.1109/tie.2022.3229344

Abstract: To alleviate the predicament of data annotating and the need for collecting data from identical distribution, unsupervised domain adaptation technologies have been widely deployed in the field of machine fault diagnosis. Nevertheless, most of them… read more here.

Keywords: fault diagnosis; adaptation; self training; adversarial adaptation ... See more keywords

Machine Fault Diagnosis Method Using Lightweight 1-D Separable Convolution and WSNs With Sensor Computing

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recommendations! Published in 2022 at "IEEE Transactions on Instrumentation and Measurement"

DOI: 10.1109/tim.2022.3206764

Abstract: Compared with a wired machine fault diagnosis system, a wireless one based on wireless sensor networks (WSNs) has many inherent merits, such as low cost and ease of installation. However, the limited bandwidth and battery… read more here.

Keywords: diagnosis; fault diagnosis; machine fault; method ... See more keywords

FedAlign: Federated Model Alignment via Data-Free Knowledge Distillation for Machine Fault Diagnosis

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recommendations! Published in 2024 at "IEEE Transactions on Instrumentation and Measurement"

DOI: 10.1109/tim.2023.3345910

Abstract: Due to privacy issues, the data island problem of machine fault diagnosis widely exists in real industry. Federated learning (FL) has received much attention as a decentralized machine-learning paradigm that learns a global model in… read more here.

Keywords: machine fault; data free; fault diagnosis; fault ... See more keywords

Vibrational Triboelectric Nanogenerator-Based Multinode Self-Powered Sensor Network for Machine Fault Detection

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recommendations! Published in 2020 at "IEEE/ASME Transactions on Mechatronics"

DOI: 10.1109/tmech.2020.2993336

Abstract: Physical parameter sensing largely benefits the lifetime and operational costs of machines and has been widely used for machine fault detection. Herein, in this article, we developed a multinode sensor network, which is fully self-powered… read more here.

Keywords: machine fault; fault detection; machine; self powered ... See more keywords

Machine Fault Diagnosis through Vibration Analysis: Time Series Conversion to Grayscale and RGB Images for Recognition via Convolutional Neural Networks

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recommendations! Published in 2024 at "Energies"

DOI: 10.3390/en17091998

Abstract: Accurate and timely fault detection is crucial for ensuring the smooth operation and longevity of rotating machinery. This study explores the effectiveness of image-based approaches for machine fault diagnosis using data from a 6DOF IMU… read more here.

Keywords: machine fault; fault diagnosis; fault; rgb ... See more keywords