LeiYYangBJiangX, et al. Applications of machine learning to machine fault diagnosis: A review and roadmap. Mech Syst Sign Process2020; 1(138): 106587.
2.
FinkOWangQSvensenM, et al. Potential, challenges and future directions for deep learning in prognostics and health management applications. Eng Appl Artif Intel2020; 1(92): 103678.
3.
KumarAGandhiCPLiuX, et al. A novel health indicator developed using filter-based feature selection algorithm for the identification of rotor defects. Proceed Instit Mech Eng Part O J Risk Reliab. 2020. DOI: 10.1177/1748006X20916953.
4.
SunYKangJSunL, et al. Condition-based maintenance for the offshore wind turbine based on long short-term memory network. Proceed Instit Mech Eng Part O J Risk Reliab. 2020. DOI: 10.1177/1748006X20965434.
5.
LuYMiJLiangH, et al. Intelligent fault diagnosis of rotating machinery based on a novel lightweight convolutional neural network. Proceed Instit Mech Eng Part O J Risk Reliab. 2020. DOI: 10.1177/1748006X20965016.
6.
DroguettETapiaJYanezC, et al. Semantic segmentation model for crack images from concrete bridges for mobile devices. Proceed Instit Mech Eng Part O J Risk Reliab. 2020. DOI: 10.1177/1748006X20965111.
7.
Davila-FriasAYadavOP. All-terminal network reliability estimation using convolutional neural networks. Proceed Instit Mech Eng Part O J Risk Reliab2020. DOI: 10.1177/1748006X20969465.
8.
FigueroaBJGuardaBLBenitesPR, et al. Deep learning health state prognostics of physical assets in the oil and gas industry. Proceed Instit Mech Eng Part O J Risk Reliab. 2021. DOI: 10.1177/1748006X20976817.
9.
GarciaGRMichauGDucoffeM, et al. Temporal signals to images: Monitoring the condition of industrial assets with deep learning image processing algorithms. Proceed Instit Mech Eng Part O J Risk Reliab2021. DOI: 10.1177/1748006X21994446.
10.
ZhouTDroguettELMoslehA, et al. An uncertainty-informed framework for trustworthy fault diagnosis in safety-critical applications. 2021. arXiv preprint arXiv: 2111.00874.
11.
CaceresJGonzalezDZhouT, et al. A probabilistic Bayesian recurrent neural network for remaining useful life prognostics considering epistemic and aleatory uncertainties. Struct Control Health Monit2021; 28(10): e2811. DOI: 10.1002/stc.2811.
12.
WangDChenYShenC, et al. Fully interpretable neural network for locating resonance frequency bands for machine condition monitoring. Mech Syst Sig Process2022; 1(168): 108673.
13.
HouBWangDChenY, et al. Interpretable online updated weights: Optimized square envelope spectrum for machine condition monitoring and fault diagnosis. Mech Syst Sig Process2022; 15(169): 108779.
14.
ZhouTDroguettELMoslehA. Physics-informed deep learning: a promising technique for system reliability assessment. 2021. arXiv preprint arXiv:2108.10828.
