Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders | Paper Citation Record

Observation 9ef303c3-c592-4645-8d46-6e078e18d929 · outbound

This paper cites Applications of machine learning to machine fault diagnosis: A review and roadmap.Mechanical systems and signal processing, 138:106587, 2020.

Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders Applications of machine learning to machine fault diagnosis: A review and roadmap.Mechanical systems and signal processing, 138:106587, 2020

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Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders Unresolved cited work

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This paper cites A transferable diagnosis method with incipient fault detection for a digital twin of wind turbine.Digital Engineering, 1:100001, 2024.

Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders A transferable diagnosis method with incipient fault detection for a digital twin of wind turbine.Digital Engineering, 1:100001, 2024

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This paper cites From theory to industry: A survey of deep learning-enabled bearing fault diagnosis in complex environments.Engineering Applications of Artificial Intelligence, 163:113068, 2026.

Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders From theory to industry: A survey of deep learning-enabled bearing fault diagnosis in complex environments.Engineering Applications of Artificial Intelligence, 163:113068, 2026

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This paper cites Deep learn- ing and its applications to machine health monitoring.Mechanical systems and signal processing, 115:213–237, 2019.

Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders Deep learn- ing and its applications to machine health monitoring.Mechanical systems and signal processing, 115:213–237, 2019

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Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders Unresolved cited work

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This paper cites A lightweight hybrid model-based condition monitoring method for grinding wheels using acoustic emission signals.Measurement Science and Technology, 36(1):016145, 2025.

Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders A lightweight hybrid model-based condition monitoring method for grinding wheels using acoustic emission signals.Measurement Science and Technology, 36(1):016145, 2025

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Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders Unresolved cited work

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This paper cites Opensetrecognition methods for fault diagnosis: A review.

Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders Opensetrecognition methods for fault diagnosis: A review

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This paper cites Center margin loss-based uncertainty-aware fault diagnosis for rotating machines to identify unseen faults.Journal of Computational Design and Engineering, page qwag057, 2026.

Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders Center margin loss-based uncertainty-aware fault diagnosis for rotating machines to identify unseen faults.Journal of Computational Design and Engineering, page qwag057, 2026

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Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders Unresolved cited work

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This paper cites From coarse to fine-grained open-set recognition.

Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders From coarse to fine-grained open-set recognition

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This paper cites Rolling element bearing diagnostics using the case western reserve university data: A benchmark study.Mechanical systems and signal processing, 64:100– 131, 2015.

Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders Rolling element bearing diagnostics using the case western reserve university data: A benchmark study.Mechanical systems and signal processing, 64:100– 131, 2015

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Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders Towards open set deep networks

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This paper cites An adaptive expansion network for incremental fault diagnosis in open and dynamic industrial systems.Engineering Applications of Artificial Intelligence, 181:115589, 2026.

Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders An adaptive expansion network for incremental fault diagnosis in open and dynamic industrial systems.Engineering Applications of Artificial Intelligence, 181:115589, 2026

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This paper cites Convolutional pro- totype network for open set recognition.IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(5):2358–2370, 2020.

Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders Convolutional pro- totype network for open set recognition.IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(5):2358–2370, 2020

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This paper cites Adversarial reciprocal points learning for open set recognition.IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 44(11):8065–8081, 2021.

Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders Adversarial reciprocal points learning for open set recognition.IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 44(11):8065–8081, 2021

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This paper cites A comparative study of time–frequency representations for bearing and rotating fault diagnosis using vision transformer.Machines, 13(8):737, 2025.

Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders A comparative study of time–frequency representations for bearing and rotating fault diagnosis using vision transformer.Machines, 13(8):737, 2025

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This paper cites Frequency-enhanced neural networks with a hybrid spall-size estimator for bearing fault diagnosis.Journal of Computational Design and Engineering, 12(5):1–20, 2025.

Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders Frequency-enhanced neural networks with a hybrid spall-size estimator for bearing fault diagnosis.Journal of Computational Design and Engineering, 12(5):1–20, 2025

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Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders An efficient adaptive window size selection method for improving spectrogram visualization.Computational intelligence and neu- roscience, 2016(1):6172453, 2016

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This paper cites A fault information-guided variational mode decomposition (fivmd) method for rolling element bearings diagnosis.Mechanical Systems and Signal Processing, 164:108216, 2022.

Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders A fault information-guided variational mode decomposition (fivmd) method for rolling element bearings diagnosis.Mechanical Systems and Signal Processing, 164:108216, 2022

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Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders A simple unified framework for detecting out-of-distribution samples and adversarial attacks.Advances in neural information processing systems, 31, 2018

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Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders Mahalanobis++: Improving OOD Detection via Feature Normalization

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