Research graph
References from Multimodal heterogeneous feature learning with modality-cross-attention for robust fault diagnosis of machines under complex conditions. Local targets link to admitted publications; unresolved targets remain external evidence.
A novel bearing intelligent fault diagnosis method based on spectrum sparse deep deconvolution
10.1016/j.engappai.2024.108102 · 2024 · External reference
Intelligent fault diagnosis of ultrasonic motors based on graph-regularized CNN-BiLSTM
10.1088/1361-6501/ad28e8 · 2024 · External reference
Transient-aware spectral segmentation informed by energy-distribution modeling and optimized band selection for railway bogie fault diagnosis
10.1016/j.conengprac.2026.107009 · 2026 · External reference
Vibration-weighted maximum correlated kurtosis deconvolution and latent cyclic pattern discovery for fault diagnosis of high-speed rail bogies
10.1016/j.jsv.2026.119657 · 2026 · External reference
Markovian spectral transition modeling with temporal dependencies for railway bogie axle bearing diagnostics in non-stationary transient environments
10.1007/s11071-025-12114-y · 2026 · External reference
Cyclic spectral coherence map-based filter design with geometric mean optimization for industrial fault diagnosis
2026 · External reference
Fault detection of the rotating machines through optimized orthogonal matching pursuit by Golden Jackal Optimization
2025 · External reference
Intelligent fault diagnosis of gearbox under variable working conditions with adaptive intraclass and interclass convolutional neural network
10.1109/tnnls.2021.3135877 · 2022 · External reference
Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review
10.1007/s10489-022-03344-3 · 2022 · External reference
Prior knowledge-augmented self-supervised feature learning for few-shot intelligent fault diagnosis of machines
10.1109/tie.2022.3140403 · 2022 · External reference
A review of the application of deep learning in intelligent fault diagnosis of rotating machinery
10.1016/j.measurement.2022.112346 · 2023 · External reference
Prior knowledge-informed multi-task dynamic learning for few-shot machinery fault diagnosis
10.1016/j.eswa.2025.126439 · 2025 · External reference
A novel reinforcement learning agent for rotating machinery fault diagnosis with data augmentation
10.1016/j.ress.2024.110570 · 2025 · External reference
Environment adaptive deep reinforcement learning for intelligent fault diagnosis
10.1016/j.engappai.2025.110783 · 2025 · External reference
Vibration-based intelligent fault diagnosis for roller bearings in low-speed rotating machinery
10.1109/tim.2018.2806984 · 2018 · External reference
An intelligent fault diagnosis framework for raw vibration signals: adaptive overlapping convolutional neural network
10.1088/1361-6501/aad101 · 2018 · External reference
Multi-sensor data fusion for rotating machinery fault detection using improved cyclic spectral covariance matrix and motor current signal analysis
10.1016/j.ress.2022.108969 · 2023 · External reference
Cross-modal fusion convolutional neural networks with online soft-label training strategy for mechanical fault diagnosis
10.1109/tii.2023.3256400 · 2023 · External reference
A residual multihead self-attention network using multimodal shallow feature fusion for motor fault diagnosis
10.1109/jsen.2023.3322151 · 2023 · External reference
A liquid-impulse neural network model based on heterogeneous fusion of multimodal information for interpretable rotating machinery fault diagnosis
10.1016/j.ymssp.2026.113923 · 2026 · External reference
Multimodal-based model for asynchronous motor fault diagnosis under noisy and variable operating conditions: a novel hybrid approach
10.1016/j.ymssp.2026.113898 · 2026 · External reference
Hybrid multimodal fusion with deep learning for rolling bearing fault diagnosis
10.1016/j.measurement.2020.108655 · 2021 · External reference
Multimodal convolutional neural network model with information fusion for intelligent fault diagnosis in rotating machinery
10.1088/1361-6501/ac7eb0 · 2022 · External reference
Homotypic multi-source joint representation with dynamic hierarchical feature tracing in hybrid Walsh–frequency domain for prior knowledge-constrained fault diagnosis of multi-cylinder hydraulic pumps
10.1016/j.ymssp.2026.114450 · 2026 · External reference
Multimodal fusion fault diagnosis method under noise interference
10.1016/j.apacoust.2024.110301 · 2025 · External reference
Novel three-stage feature fusion method of multimodal data for bearing fault diagnosis
2021 · External reference
Fault diagnosis of multimodal feature fusion convolutional neural network based on differential evolution optimization
10.1016/j.compeleceng.2025.110518 · 2025 · External reference
Robust multimodal fault diagnosis for rod pumping systems via temporal convolutional network and multi-task learning
10.1016/j.neucom.2025.131499 · 2025 · External reference
Multimodal unified generalization and translation network for intelligent fault diagnosis under dynamic environments
10.1016/j.engappai.2025.112559 · 2025 · External reference
10.1145/3292500.3330701
10.1145/3292500.3330701 · External reference
University of ottawa constant load and speed rolling-element bearing vibration and acoustic fault signature datasets
10.1016/j.dib.2023.109327 · 2023 · External reference
MRCFN: A multi-sensor residual convolutional fusion network for intelligent fault diagnosis of bearings in noisy and small sample scenarios
10.1016/j.eswa.2024.125214 · 2025 · External reference
Vibration and acoustic signal consistent feature fusion network for intelligent bearing fault diagnosis
10.1088/2631-8695/ade849 · 2025 · External reference
CDTFAFN: A novel coarse-to-fine dual-scale time-frequency attention fusion network for machinery vibro-acoustic fault diagnosis
10.1016/j.inffus.2024.102554 · 2024 · External reference
MSF-dformer: A multi-sensor multi-scale fusion network with deformable transformer for fault diagnosis under complex working conditions with limited samples
10.1109/tim.2025.3615270 · 2025 · External reference
An integrated framework for bearing fault diagnosis: convolutional neural network model compression through knowledge distillation
10.1109/jsen.2024.3481298 · 2024 · External reference
Support-vector networks
10.1023/a:1022627411411 · 1995 · External reference
LIBSVM: A library for support vector machines
10.1145/1961189.1961199 · 2011 · External reference
Vibration, acoustic, temperature, and motor current dataset of rotating machine under varying operating conditions for fault diagnosis
10.1016/j.dib.2023.109049 · 2023 · External reference
Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review
10.1007/s10489-022-03344-3 · ExternalCitation · doi-reference
Markovian spectral transition modeling with temporal dependencies for railway bogie axle bearing diagnostics in non-stationary transient environments
10.1007/s11071-025-12114-y · ExternalCitation · doi-reference
Multimodal fusion fault diagnosis method under noise interference
10.1016/j.apacoust.2024.110301 · ExternalCitation · doi-reference
Fault diagnosis of multimodal feature fusion convolutional neural network based on differential evolution optimization
10.1016/j.compeleceng.2025.110518 · ExternalCitation · doi-reference
Transient-aware spectral segmentation informed by energy-distribution modeling and optimized band selection for railway bogie fault diagnosis
10.1016/j.conengprac.2026.107009 · ExternalCitation · doi-reference
Vibration, acoustic, temperature, and motor current dataset of rotating machine under varying operating conditions for fault diagnosis
10.1016/j.dib.2023.109049 · ExternalCitation · doi-reference
University of ottawa constant load and speed rolling-element bearing vibration and acoustic fault signature datasets
10.1016/j.dib.2023.109327 · ExternalCitation · doi-reference
A novel bearing intelligent fault diagnosis method based on spectrum sparse deep deconvolution
10.1016/j.engappai.2024.108102 · ExternalCitation · doi-reference
Environment adaptive deep reinforcement learning for intelligent fault diagnosis
10.1016/j.engappai.2025.110783 · ExternalCitation · doi-reference
Multimodal unified generalization and translation network for intelligent fault diagnosis under dynamic environments
10.1016/j.engappai.2025.112559 · ExternalCitation · doi-reference
MRCFN: A multi-sensor residual convolutional fusion network for intelligent fault diagnosis of bearings in noisy and small sample scenarios
10.1016/j.eswa.2024.125214 · ExternalCitation · doi-reference
Prior knowledge-informed multi-task dynamic learning for few-shot machinery fault diagnosis
10.1016/j.eswa.2025.126439 · ExternalCitation · doi-reference
CDTFAFN: A novel coarse-to-fine dual-scale time-frequency attention fusion network for machinery vibro-acoustic fault diagnosis
10.1016/j.inffus.2024.102554 · ExternalCitation · doi-reference
Vibration-weighted maximum correlated kurtosis deconvolution and latent cyclic pattern discovery for fault diagnosis of high-speed rail bogies
10.1016/j.jsv.2026.119657 · ExternalCitation · doi-reference
Hybrid multimodal fusion with deep learning for rolling bearing fault diagnosis
10.1016/j.measurement.2020.108655 · ExternalCitation · doi-reference
A review of the application of deep learning in intelligent fault diagnosis of rotating machinery
10.1016/j.measurement.2022.112346 · ExternalCitation · doi-reference
Robust multimodal fault diagnosis for rod pumping systems via temporal convolutional network and multi-task learning
10.1016/j.neucom.2025.131499 · ExternalCitation · doi-reference
Multi-sensor data fusion for rotating machinery fault detection using improved cyclic spectral covariance matrix and motor current signal analysis
10.1016/j.ress.2022.108969 · ExternalCitation · doi-reference
A novel reinforcement learning agent for rotating machinery fault diagnosis with data augmentation
10.1016/j.ress.2024.110570 · ExternalCitation · doi-reference
Multimodal-based model for asynchronous motor fault diagnosis under noisy and variable operating conditions: a novel hybrid approach
10.1016/j.ymssp.2026.113898 · ExternalCitation · doi-reference
A liquid-impulse neural network model based on heterogeneous fusion of multimodal information for interpretable rotating machinery fault diagnosis
10.1016/j.ymssp.2026.113923 · ExternalCitation · doi-reference
Homotypic multi-source joint representation with dynamic hierarchical feature tracing in hybrid Walsh–frequency domain for prior knowledge-constrained fault diagnosis of multi-cylinder hydraulic pumps
10.1016/j.ymssp.2026.114450 · ExternalCitation · doi-reference
Support-vector networks
10.1023/a:1022627411411 · ExternalCitation · doi-reference
An intelligent fault diagnosis framework for raw vibration signals: adaptive overlapping convolutional neural network
10.1088/1361-6501/aad101 · ExternalCitation · doi-reference
Multimodal convolutional neural network model with information fusion for intelligent fault diagnosis in rotating machinery
10.1088/1361-6501/ac7eb0 · ExternalCitation · doi-reference
Intelligent fault diagnosis of ultrasonic motors based on graph-regularized CNN-BiLSTM
10.1088/1361-6501/ad28e8 · ExternalCitation · doi-reference
Vibration and acoustic signal consistent feature fusion network for intelligent bearing fault diagnosis
10.1088/2631-8695/ade849 · ExternalCitation · doi-reference
A residual multihead self-attention network using multimodal shallow feature fusion for motor fault diagnosis
10.1109/jsen.2023.3322151 · ExternalCitation · doi-reference
An integrated framework for bearing fault diagnosis: convolutional neural network model compression through knowledge distillation
10.1109/jsen.2024.3481298 · ExternalCitation · doi-reference
Prior knowledge-augmented self-supervised feature learning for few-shot intelligent fault diagnosis of machines
10.1109/tie.2022.3140403 · ExternalCitation · doi-reference
Cross-modal fusion convolutional neural networks with online soft-label training strategy for mechanical fault diagnosis
10.1109/tii.2023.3256400 · ExternalCitation · doi-reference
Vibration-based intelligent fault diagnosis for roller bearings in low-speed rotating machinery
10.1109/tim.2018.2806984 · ExternalCitation · doi-reference
MSF-dformer: A multi-sensor multi-scale fusion network with deformable transformer for fault diagnosis under complex working conditions with limited samples
10.1109/tim.2025.3615270 · ExternalCitation · doi-reference
Intelligent fault diagnosis of gearbox under variable working conditions with adaptive intraclass and interclass convolutional neural network
10.1109/tnnls.2021.3135877 · ExternalCitation · doi-reference
LIBSVM: A library for support vector machines
10.1145/1961189.1961199 · ExternalCitation · doi-reference
10.1145/3292500.3330701
10.1145/3292500.3330701 · ExternalCitation · doi-reference
