Fault Diagnosis
59 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Most implemented papers
ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions
This paper proposes a new large-scale dataset called "ToyADMOS2" for anomaly detection in machine operating sounds (ADMOS).
Bearing Fault Diagnosis Base on Multi-scale CNN and LSTM Model
Xiaohan-Chen/bear_fault_diagnosis
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journal 2020
Intelligent fault diagnosis methods based on signal analysis have been widely used for bearing fault diagnosis.
ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection
To build a large-scale dataset for ADMOS, we collected anomalous operating sounds of miniature machines (toys) by deliberately damaging them.
Missing-Class-Robust Domain Adaptation by Unilateral Alignment for Fault Diagnosis
diagnosisda/dxda
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We demonstrate in this paper that the performance of domain adversarial methods can be vulnerable to an incomplete target label space during training.
GPLA-12: An Acoustic Signal Dataset of Gas Pipeline Leakage
In this paper, we introduce a new acoustic leakage dataset of gas pipelines, called as GPLA-12, which has 12 categories over 684 training/testing acoustic signals.
Lighter, Better, Faster Multi-Source Domain Adaptation with Gaussian Mixture Models and Optimal Transport
eddardd/PyDiL
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In this paper, we tackle Multi-Source Domain Adaptation (MSDA), a task in transfer learning where one adapts multiple heterogeneous, labeled source probability measures towards a different, unlabeled target measure.
BearLLM: A Prior Knowledge-Enhanced Bearing Health Management Framework with Unified Vibration Signal Representation
sia-ide/bearllm
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This involves adaptively sampling the vibration signals based on the sampling rate of the sensor, incorporating the frequency domain to unify input dimensions, and using a fault-free reference signal as an auxiliary input.
Limited Data Rolling Bearing Fault Diagnosis with Few-shot Learning
SNBQT/Limited-Data-Rolling-Bearing-Fault-Diagnosis-with-Few-shot-Learning
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IEEE Access 2019
In this study, we propose a deep neural network based few-shot learning approach for rolling bearing fault diagnosis with limited data.
WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis
In this paper, a novel wavelet driven deep neural network termed as WaveletKernelNet (WKN) is presented, where a continuous wavelet convolutional (CWConv) layer is designed to replace the first convolutional layer of the standard CNN.
Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study
ZhaoZhibin/UDTL
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Besides, the newly collected test data in the target domain are usually unlabeled, leading to unsupervised deep transfer learning based (UDTL-based) IFD problem.
