[1]
|
L. Jia, Q. Gao, Z. P. Liu, H. B. Tan, L. W. Zhou, Multidisciplinary fault diagnosis of complex engineering systems: A case study of nuclear power plants, Int. J. Ind. Ergon., 80 (2020), 103060. https://doi.org/10.1016/j.ergon.2020.103060 doi: 10.1016/j.ergon.2020.103060
|
[2]
|
Y. B. Li, B. Li, J. C. Ji, H. Kalhori, Advanced fault diagnosis and health monitoring techniques for complex engineering systems, Sensors, 22 (2022), 10002. https://doi.org/10.3390/s222410002 doi: 10.3390/s222410002
|
[3]
|
C. Wang, H. G. Matthies, Random model with fuzzy distribution parameters for hybrid uncertainty propagation in engineering systems, Comput. Meth. Appl. Mech. Eng., 359 (2020), 112673. https://doi.org/10.1016/j.cma.2019.112673 doi: 10.1016/j.cma.2019.112673
|
[4]
|
F. Villecco, A. Pellegrino, Evaluation of uncertainties in the design process of complex mechanical systems, Entropy, 19 (2017), e19090475. https://doi.org/10.3390/e19090475 doi: 10.3390/e19090475
|
[5]
|
E. Hüllermeier, W. Waegeman, Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods, Mach. Learn., 110 (2021), 457–506. https://doi.org/10.1007/s10994-021-05946-3 doi: 10.1007/s10994-021-05946-3
|
[6]
|
H. R. Fan, C. Wang, S. H. Li, Novel method for reliability optimization design based on rough set theory and hybrid surrogate model, Comput. Meth. Appl. Mech. Eng., 429 (2024), 117170. https://doi.org/10.1016/j.cma.2024.117170 doi: 10.1016/j.cma.2024.117170
|
[7]
|
M. Mansouri, R. Fezai, M. Trabelsi, M. Hajji, M.-F. Harkat, H. Nounou, et al., A novel fault diagnosis of uncertain systems based on interval Gaussian process regression: Application to wind energy conversion systems, IEEE Access, 8 (2020), 219672–219679. https://doi.org/10.1109/access.2020.3042101 doi: 10.1109/access.2020.3042101
|
[8]
|
X. X. Liu, Y. T. Ju, X. H. Liu, S. Miao, W. G. Zhang, An imu fault diagnosis and information reconstruction method based on analytical redundancy for autonomous underwater vehicle, IEEE Sens. J., 22 (2022), 12127–12138. https://doi.org/10.1109/jsen.2022.3174340 doi: 10.1109/jsen.2022.3174340
|
[9]
|
D. Yu, Fault diagnosis for a hydraulic drive system using a parameter-estimation method, Control Eng. Practice, 5 (1997), 1283–1291. https://doi.org/10.1016/s0967-0661(97)84367-5 doi: 10.1016/s0967-0661(97)84367-5
|
[10]
|
G. C. Zhang, L. Chen, K. K. Liang, Fault monitoring and diagnosis of aerostat actuator based on pca and state observer, Int. J. Model. Identif. Control, 32 (2019), 145. https://doi.org/10.1504/ijmic.2019.102367 doi: 10.1504/ijmic.2019.102367
|
[11]
|
Y. Song, M. Y. Zhong, J. Chen, Y. Liu, An alternative parity space-based fault diagnosability analysis approach for linear discrete time systems, IEEE Access, 6 (2018), 16110–16118. https://doi.org/10.1109/access.2018.2816970 doi: 10.1109/access.2018.2816970
|
[12]
|
V. Venkatasubramanian, R. Rengaswamy, S. N. Kavuri, A review of process fault detection and diagnosis, Comput. Chem. Eng., 27 (2003), 313–326. https://doi.org/10.1016/s0098-1354(02)00161-8 doi: 10.1016/s0098-1354(02)00161-8
|
[13]
|
S. W. Pan, D. Xiao, S. T. Xing, S. S. Law, P. Y. Du, Y. J. Li, A general extended kalman filter for simultaneous estimation of system and unknown inputs, Eng. Struct., 109 (2016), 85–98. https://doi.org/10.1016/j.engstruct.2015.11.014 doi: 10.1016/j.engstruct.2015.11.014
|
[14]
|
E. Walker, S. Rayman, R. E. White, Comparison of a particle filter and other state estimation methods for prognostics of lithium-ion batteries, J. Power Sources, 287 (2015), 1–12. https://doi.org/10.1016/j.jpowsour.2015.04.020 doi: 10.1016/j.jpowsour.2015.04.020
|
[15]
|
S. Nolan, A. Smerzi, L. Pezzè, A machine learning approach to Bayesian parameter estimation, npj Quantum Inform., 7 (2021), 169. https://doi.org/10.1038/s41534-021-00497-w doi: 10.1038/s41534-021-00497-w
|
[16]
|
R. Tarantino, F. Szigeti, E. Colina-Morles, Generalized luenberger observer-based fault-detection filter design: An industrial application, Control Eng. Practice, 8 (2000), 665–671. https://doi.org/10.1016/s0967-0661(99)00181-1 doi: 10.1016/s0967-0661(99)00181-1
|
[17]
|
L. A. Rusinov, N. V. Vorobiev, V. V. Kurkina, Fault diagnosis in chemical processes and equipment with feedbacks, Chemometrics Intell. Lab. Syst., 126 (2013), 123–128. https://doi.org/10.1016/j.chemolab.2013.03.015 doi: 10.1016/j.chemolab.2013.03.015
|
[18]
|
F. Pierri, G. Paviglianiti, F. Caccavale, M. Mattei, Observer-based sensor fault detection and isolation for chemical batch reactors, Eng. Appl. Artif. Intell., 21 (2008), 1204–1216. https://doi.org/10.1016/j.engappai.2008.02.002 doi: 10.1016/j.engappai.2008.02.002
|
[19]
|
H. M. Odendaal, T. Jones, Actuator fault detection and isolation: An optimised parity space approach, Control Eng. Practice, 26 (2014), 222–232. https://doi.org/10.1016/j.conengprac.2014.01.013 doi: 10.1016/j.conengprac.2014.01.013
|
[20]
|
C. J. Duan, Z. Y. Fei, J. C. Li, A variable selection aided residual generator design approach for process control and monitoring, Neurocomputing, 171 (2016), 1013–1020. https://doi.org/10.1016/j.neucom.2015.07.042 doi: 10.1016/j.neucom.2015.07.042
|
[21]
|
P. Zhang, S. X. Ding, Disturbance decoupling in fault detection of linear periodic systems, Automatica, 43 (2007), 1410–1417. https://doi.org/10.1016/j.automatica.2007.01.005 doi: 10.1016/j.automatica.2007.01.005
|
[22]
|
Q. Wang, C. Taal, O. Fink, Integrating expert knowledge with domain adaptation for unsupervised fault diagnosis, IEEE Trans. Instrum. Meas., 71 (2022), 1–12. https://doi.org/10.1109/tim.2021.3127654 doi: 10.1109/tim.2021.3127654
|
[23]
|
P. Zhao, X. D. Mu, Z. R. Yin, Z. X. Yi, An approach of fault diagnosis for system based on fuzzy fault tree, 2008 International Conference on MultiMedia and Information Technology, Three Gorges, China, 2008,697–700. https://doi.org/10.1109/mmit.2008.142
|
[24]
|
Z. N. Lin, Y. X. Wang, H. Q. Xu, F. R. Wei, A novel reduced-order analytical fault diagnosis model for power grid, IEEE Access, 12 (2024), 59521–59532. https://doi.org/10.1109/access.2024.3392905 doi: 10.1109/access.2024.3392905
|
[25]
|
C. Cheng, X. Y. Qiao, H. Luo, W. X. Teng, M. L. Gao, B. C. Zhang, et al., A semi-quantitative information based fault diagnosis method for the running gears system of high-speed trains, IEEE Access, 7 (2019), 38168–38178. https://doi.org/10.1109/access.2019.2906976 doi: 10.1109/access.2019.2906976
|
[26]
|
J. P. Shi, W. G. Tong, D. L. Wang, Design of the transformer fault diagnosis expert system based on fuzzy reasoning, 2009 International Forum on Computer Science-Technology and Applications, Chongqing, China, 2009,110–114. https://doi.org/10.1109/ifcsta.2009.34
|
[27]
|
A. R. Sahu, S. K. Palei, A. Mishra, Data-driven fault diagnosis approaches for industrial equipment: A review, Expert Syst., 41 (2024), 13360. https://doi.org/10.1111/exsy.13360 doi: 10.1111/exsy.13360
|
[28]
|
G. Wang, J. Y. Zhao, J. H. Yang, J. F. Jiao, J. L. Xie, F. Feng, Multivariate statistical analysis based cross voltage correlation method for internal short-circuit and sensor faults diagnosis of lithium-ion battery system, J. Energy Storage, 62 (2023), 106978. https://doi.org/10.1016/j.est.2023.106978 doi: 10.1016/j.est.2023.106978
|
[29]
|
Z. Zhang, X. He, Active fault diagnosis for linear systems: Within a signal processing framework, IEEE Trans. Instrum. Meas., 71 (2022), 1–9. https://doi.org/10.1109/tim.2022.3150889 doi: 10.1109/tim.2022.3150889
|
[30]
|
R. N. Liu, B. Y. Yang, E. Zio, X. F. Chen, Artificial intelligence for fault diagnosis of rotating machinery: A review, Mech. Syst. Signal Proc., 108 (2018), 33–47. https://doi.org/10.1016/j.ymssp.2018.02.016 doi: 10.1016/j.ymssp.2018.02.016
|
[31]
|
Y. Q. Liu, B. Liu, X. J. Zhao, M. Xie, A mixture of variational canonical correlation analysis for nonlinear and quality-relevant process monitoring, IEEE Trans. Ind. Electron., 65 (2018), 6478–6486. https://doi.org/10.1109/tie.2017.2786253 doi: 10.1109/tie.2017.2786253
|
[32]
|
G. Lee, C. H. Han, E. S. Yoon, Multiple-fault diagnosis of the tennessee eastman process based on system decomposition and dynamic pls, Ind. Eng. Chem. Res., 43 (2004), 8037–8048. https://doi.org/10.1021/ie049624u doi: 10.1021/ie049624u
|
[33]
|
G. Yu, C. N. Li, J. Sun, Machine fault diagnosis based on Gaussian mixture model and its application, Int. J. Adv. Manuf. Technol., 48 (2010), 205–212. https://doi.org/10.1007/s00170-009-2283-5 doi: 10.1007/s00170-009-2283-5
|
[34]
|
W. Deng, S. J. Zhang, H. M. Zhao, X. H. Yang, A novel fault diagnosis method based on integrating empirical wavelet transform and fuzzy entropy for motor bearing, IEEE Access, 6 (2018), 35042–35056. https://doi.org/10.1109/access.2018.2834540 doi: 10.1109/access.2018.2834540
|
[35]
|
J. B. Guo, Fault diagnosis method of flexible converter valve equipment based on ensemble empirical mode decomposition and temporal convolutional networks, J. Electr. Syst., 20 (2024), 344–352. https://doi.org/10.52783/jes.2386 doi: 10.52783/jes.2386
|
[36]
|
D. J. Yu, M. Wang, X. M. Cheng, A method for the compound fault diagnosis of gearboxes based on morphological component analysis, Measurement, 91 (2016), 519–531. https://doi.org/10.1016/j.measurement.2016.05.087 doi: 10.1016/j.measurement.2016.05.087
|
[37]
|
L. Ciabattoni, F. Ferracuti, A. Freddi, A. Monteriu, Statistical spectral analysis for fault diagnosis of rotating machines, IEEE Trans. Ind. Electron., 65 (2018), 4301–4310. https://doi.org/10.1109/tie.2017.2762623 doi: 10.1109/tie.2017.2762623
|
[38]
|
W. E. Sanders, T. Burton, A. Khosousi, S. Ramchandani, Machine learning: At the heart of failure diagnosis, Curr. Opin. Cardiol., 36 (2021), 227–233. https://doi.org/10.1097/hco.0000000000000833 doi: 10.1097/hco.0000000000000833
|
[39]
|
Y. G. Lei, B. Yang, X. W. Jiang, F. Jia, N. P. Li, A. K. Nandi, Applications of machine learning to machine fault diagnosis: A review and roadmap, Mech. Syst. Signal Proc., 138 (2020), 106587. https://doi.org/10.1016/j.ymssp.2019.106587 doi: 10.1016/j.ymssp.2019.106587
|
[40]
|
Z. N. An, F. Wu, C. Zhang, J. H. Ma, B. Sun, B. H. Tang, et al., Deep learning-based composite fault diagnosis, IEEE Jour. Emer. Select. Top. Circu. Syste., 13 (2023), 572–581. https://doi.org/10.1109/jetcas.2023.3262241 doi: 10.1109/jetcas.2023.3262241
|
[41]
|
D. T. Hoang, H. J. Kang, A survey on deep learning based bearing fault diagnosis, Neurocomputing, 335 (2019), 327–335. https://doi.org/10.1016/j.neucom.2018.06.078 doi: 10.1016/j.neucom.2018.06.078
|
[42]
|
X. Y. Fan, J. Li, H. Hao, Review of piezoelectric impedance based structural health monitoring: Physics-based and data-driven methods, Adv. Struct. Eng., 24 (2021), 3609–3626. https://doi.org/10.1177/13694332211038444 doi: 10.1177/13694332211038444
|
[43]
|
Q. Ni, X. M. Li, Z. W. Chen, Z. L. Zhao, L. L. Lai, A mechanism and data hybrid-driven method for main circuit ground fault diagnosis in electrical traction system, IEEE Trans. Ind. Electron., 70 (2023), 12806–12815. https://doi.org/10.1109/tie.2023.3260356 doi: 10.1109/tie.2023.3260356
|
[44]
|
D. An, N. H. Kim, J. H. Choi, Practical options for selecting data-driven or physics-based prognostics algorithms with reviews, Reliab. Eng. Syst. Saf., 133 (2015), 223–236. https://doi.org/10.1016/j.ress.2014.09.014 doi: 10.1016/j.ress.2014.09.014
|
[45]
|
J. Guo, Z. Y. Li, M. Y. Li, A review on prognostics methods for engineering systems, IEEE Trans. Reliab., 69 (2020), 1110–1129. https://doi.org/10.1109/tr.2019.2957965 doi: 10.1109/tr.2019.2957965
|
[46]
|
L. Kou, C. Liu, G. W. Cai, J. N. Zhou, Q. D. Yuan, S. M. Pang, Fault diagnosis for open-circuit faults in npc inverter based on knowledge-driven and data-driven approaches, IET Power Electron., 13 (2020), 1236–1245. https://doi.org/10.1049/iet-pel.2019.0835 doi: 10.1049/iet-pel.2019.0835
|
[47]
|
X. X. Xiao, C. H. Li, J. Huang, T. Yu, Fault diagnosis of rolling bearing based on knowledge graph with data accumulation strategy, IEEE Sens. J., 22 (2022), 18831–18840. https://doi.org/10.1109/JSEN.2022.3201839 doi: 10.1109/JSEN.2022.3201839
|
[48]
|
K. Sachin, M. Torres, Y. C. Chan, M. Pecht, A hybrid prognostics methodology for electronic products, 2008 IEEE International Joint Conference on Neural Networks, Hong Kong, China, 2008, 3479–3485. https://doi.org/10.1109/IJCNN.2008.4634294
|
[49]
|
S. F. Cheng, M. Pecht, A fusion prognostics method for remaining useful life prediction of electronic products, 2009 IEEE International Conference on Automation Science and Engineering, Bangalore, India, 2009,102–107. https://doi.org/10.1109/COASE.2009.5234098
|
[50]
|
H. G. Zhang, R. Kang, M. Pecht, A hybrid prognostics and health management approach for condition-based maintenance, 2009 IEEE International Conference on Industrial Engineering and Engineering Management, Hong Kong, China, 2009, 1165–1169. https://doi.org/10.1109/ieem.2009.5372976
|
[51]
|
M. A. Chao, C. Kulkarni, K. Goebel, O. Fink, Fusing physics-based and deep learning models for prognostics, Reliab. Eng. Syst. Saf., 217 (2022), 107961. https://doi.org/10.1016/j.ress.2021.107961 doi: 10.1016/j.ress.2021.107961
|
[52]
|
T. T. Li, Y. Zhao, C. B. Zhang, J. Luo, X. J. Zhang, A knowledge-guided and data-driven method for building hvac systems fault diagnosis, Build. Environ., 198 (2021), 107850. https://doi.org/10.1016/j.buildenv.2021.107850 doi: 10.1016/j.buildenv.2021.107850
|
[53]
|
L. H. Ye, X. Ma, C. L. Wen, Rotating machinery fault diagnosis method by combining time-frequency domain features and cnn knowledge transfer, Sensors, 21 (2021), 8168. https://doi.org/10.3390/s21248168 doi: 10.3390/s21248168
|
[54]
|
W. Xu, Y. Wan, T. Y. Zuo, X. M. Sha, Transfer learning based data feature transfer for fault diagnosis, IEEE Access, 8 (2020), 76120–76129. https://doi.org/10.1109/ACCESS.2020.2989510 doi: 10.1109/ACCESS.2020.2989510
|
[55]
|
X. P. Niu, R. Z. Wang, D. Liao, S. P. Zhu, X. C. Zhang, B. Keshtegar, Probabilistic modeling of uncertainties in fatigue reliability analysis of turbine bladed disks, Int. J. Fatigue, 142 (2021), 105912. https://doi.org/10.1016/j.ijfatigue.2020.105912 doi: 10.1016/j.ijfatigue.2020.105912
|
[56]
|
M. Valdenegro-Toro, D. S. Mori, A deeper look into aleatoric and epistemic uncertainty disentanglement, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), New Orleans, LA, USA, 2022, 1508–1516. https://doi.org/10.1109/cvprw56347.2022.00157
|
[57]
|
C. Wang, H. G. Matthies, M. H. Xu, Y. L. Li, Dual interval-and-fuzzy analysis method for temperature prediction with hybrid epistemic uncertainties via polynomial chaos expansion, Comput. Meth. Appl. Mech. Eng., 336 (2018), 171–186. https://doi.org/10.1016/j.cma.2018.03.013 doi: 10.1016/j.cma.2018.03.013
|
[58]
|
A. D. Kiureghian, O. Ditlevsen, Aleatory or epistemic? Does it matter?, Struct. Saf., 31 (2009), 105–112. https://doi.org/10.1016/j.strusafe.2008.06.020 doi: 10.1016/j.strusafe.2008.06.020
|
[59]
|
M. E. Paté-Cornell, Uncertainties in risk analysis: Six levels of treatment, Reliab. Eng. Syst. Saf., 54 (1996), 95–111. https://doi.org/10.1016/s0951-8320(96)00067-1 doi: 10.1016/s0951-8320(96)00067-1
|
[60]
|
C. Wang, H. R. Fan, X. Qiang, A review of uncertainty-based multidisciplinary design optimization methods based on intelligent strategies, Symmetry-Basel, 15 (2023), 1875. https://doi.org/10.3390/sym15101875 doi: 10.3390/sym15101875
|
[61]
|
C. Wang, X. Qiang, M. H. Xu, T. Wu, Recent advances in surrogate modeling methods for uncertainty quantification and propagation, Symmetry-Basel, 14 (2022), 1219. https://doi.org/10.3390/sym14061219 doi: 10.3390/sym14061219
|
[62]
|
D. Di Francesco, M. Girolami, A. B. Duncan, M. Chryssanthopoulos, A probabilistic model for quantifying uncertainty in the failure assessment diagram, Struct. Saf., 99 (2022), 102262. https://doi.org/10.1016/j.strusafe.2022.102262 doi: 10.1016/j.strusafe.2022.102262
|
[63]
|
P. Manfredi, Probabilistic uncertainty quantification of microwave circuits using Gaussian processes, IEEE Trans. Microw. Theory Tech., 71 (2023), 2360–2372. https://doi.org/10.1109/TMTT.2022.3228953 doi: 10.1109/TMTT.2022.3228953
|
[64]
|
J. S. Wu, G. E. Apostolakis, D. Okrent, Uncertainties in system analysis: Probabilistic versus nonprobabilistic theories, Reliab. Eng. Syst. Saf., 30 (1990), 163–181. https://doi.org/10.1016/0951-8320(90)90093-3 doi: 10.1016/0951-8320(90)90093-3
|
[65]
|
B. Hu, Q. M. Gong, Y. Q. Zhang, Y. H. Yin, W. J. Chen, Characterizing uncertainty in geotechnical design of energy piles based on Bayesian theorem, Acta Geotech., 17 (2022), 4191–4206. https://doi.org/10.1007/s11440-022-01535-3 doi: 10.1007/s11440-022-01535-3
|
[66]
|
K. Yao, J. Gao, Law of large numbers for uncertain random variables, IEEE Trans. Fuzzy Syst., 24 (2016), 615–621. https://doi.org/10.1109/TFUZZ.2015.2466080 doi: 10.1109/TFUZZ.2015.2466080
|
[67]
|
C. Zhang, Q. Liu, B. Zhou, C. Y. Chung, J. Li, L. Zhu, et al., A central limit theorem-based method for dc and ac power flow analysis under interval uncertainty of renewable power generation, IEEE Trans. Sustain. Energy, 14 (2023), 563–575. https://doi.org/10.1109/TSTE.2022.3220567 doi: 10.1109/TSTE.2022.3220567
|
[68]
|
C. Wang, Z. K. Song, H. R. Fan, Novel evidence theory-based reliability analysis of functionally graded plate considering thermal stress behavior, Aerosp. Sci. Technol., 146 (2024), 108936. https://doi.org/10.1016/j.ast.2024.108936 doi: 10.1016/j.ast.2024.108936
|
[69]
|
C. Wang, Evidence-theory-based uncertain parameter identification method for mechanical systems with imprecise information, Comput. Meth. Appl. Mech. Eng., 351 (2019), 281–296. https://doi.org/10.1016/j.cma.2019.03.048 doi: 10.1016/j.cma.2019.03.048
|
[70]
|
F. Arévalo, M. P. C. Alison, M. T. Ibrahim, A. Schwung, Adaptive information fusion using evidence theory and uncertainty quantification, IEEE Access, 12 (2024), 2236–2259. https://doi.org/10.1109/ACCESS.2023.3348270 doi: 10.1109/ACCESS.2023.3348270
|
[71]
|
H. R. Bae, R. V. Grandhi, R. A. Canfield, Uncertainty quantification of structural response using evidence theory, AIAA J., 41 (2003), 2062–2068. https://doi.org/10.2514/2.1898 doi: 10.2514/2.1898
|
[72]
|
Y. He, M. Mirzargar, R. M. Kirby, Mixed aleatory and epistemic uncertainty quantification using fuzzy set theory, Int. J. Approx. Reasoning, 66 (2015), 1–15. https://doi.org/10.1016/j.ijar.2015.07.002 doi: 10.1016/j.ijar.2015.07.002
|
[73]
|
C. Wang, H. G. Matthies, Hybrid evidence-and-fuzzy uncertainty propagation under a dual-level analysis framework, Fuzzy Sets Syst., 367 (2019), 51–67. https://doi.org/10.1016/j.fss.2018.10.002 doi: 10.1016/j.fss.2018.10.002
|
[74]
|
R. M. Rodríguez, L. Martínez, V. Torra, Z. S. Xu, F. Herrera, Hesitant fuzzy sets: State of the art and future directions, Int. J. Intell. Syst., 29 (2014), 495–524. https://doi.org/10.1002/int.21654 doi: 10.1002/int.21654
|
[75]
|
S. H. Khairuddin, M. H. Hasan, M. A. Hashmani, M. H. Azam, Generating clustering-based interval fuzzy type-2 triangular and trapezoidal membership functions: A structured literature review, Symmetry-Basel, 13 (2021), 239. https://doi.org/10.3390/sym13020239 doi: 10.3390/sym13020239
|
[76]
|
C. Wang, H. R. Fan, T. Wu, Novel rough set theory-based method for epistemic uncertainty modeling, analysis and applications, Appl. Math. Model., 113 (2023), 456–474. https://doi.org/10.1016/j.apm.2022.09.002 doi: 10.1016/j.apm.2022.09.002
|
[77]
|
X. Y. Zhang, Y. Y. Yao, Tri-level attribute reduction in rough set theory, Expert Syst. Appl., 190 (2022), 116187. https://doi.org/10.1016/j.eswa.2021.116187 doi: 10.1016/j.eswa.2021.116187
|
[78]
|
F. Y. Li, Z. Luo, G. Y. Sun, N. Zhang, An uncertain multidisciplinary design optimization method using interval convex models, Eng. Optimiz., 45 (2013), 697–718. https://doi.org/10.1080/0305215x.2012.690871 doi: 10.1080/0305215x.2012.690871
|
[79]
|
H. Lü, K. Yang, X. T. Huang, W.-B. Shangguan, K. G. Zhao, Uncertainty and correlation propagation analysis of powertrain mounting systems based on multi-ellipsoid convex model, Mech. Syst. Signal Proc., 173 (2022), 109058. https://doi.org/10.1016/j.ymssp.2022.109058 doi: 10.1016/j.ymssp.2022.109058
|
[80]
|
X. Qiang, C. Wang, H. R. Fan, Hybrid interval model for uncertainty analysis of imprecise or conflicting information, Appl. Math. Model., 129 (2024), 837–856. https://doi.org/10.1016/j.apm.2024.02.014 doi: 10.1016/j.apm.2024.02.014
|
[81]
|
C. Wang, X. Qiang, H. R. Fan, T. Wu, Y. L. Chen, Novel data-driven method for non-probabilistic uncertainty analysis of engineering structures based on ellipsoid model, Comput. Meth. Appl. Mech. Eng., 394 (2022), 114889. https://doi.org/10.1016/j.cma.2022.114889 doi: 10.1016/j.cma.2022.114889
|
[82]
|
C. Wang, H. G. Matthies, A modified parallelepiped model for non-probabilistic uncertainty quantification and propagation analysis, Comput. Meth. Appl. Mech. Eng., 369 (2020), 113209. https://doi.org/10.1016/j.cma.2020.113209 doi: 10.1016/j.cma.2020.113209
|
[83]
|
C. Wang, L. Hong, X. Qiang, M. H. Xu, Novel numerical method for uncertainty analysis of coupled vibro-acoustic problem considering thermal stress, Comput. Meth. Appl. Mech. Eng., 420 (2024), 116727. https://doi.org/10.1016/j.cma.2023.116727 doi: 10.1016/j.cma.2023.116727
|
[84]
|
L. X. Cao, J. Liu, L. Xie, C. Jiang, R. G. Bi, Non-probabilistic polygonal convex set model for structural uncertainty quantification, Analog Integr. Circuits Process., 89 (2021), 504–518. https://doi.org/10.1016/j.apm.2020.07.025 doi: 10.1016/j.apm.2020.07.025
|
[85]
|
L. P. Zhu, I. Elishakoff, J. H. Starnes, Derivation of multi-dimensional ellipsoidal convex model for experimental data, Math. Comput. Model., 24 (1996), 103–114. https://doi.org/10.1016/0895-7177(96)00094-5 doi: 10.1016/0895-7177(96)00094-5
|
[86]
|
C. Jiang, X. Han, G. Y. Lu, J. Liu, Z. Zhang, Y. C. Bai, Correlation analysis of non-probabilistic convex model and corresponding structural reliability technique, Comput. Meth. Appl. Mech. Eng., 200 (2011), 2528–2546. https://doi.org/10.1016/j.cma.2011.04.007 doi: 10.1016/j.cma.2011.04.007
|
[87]
|
J. Liu, Z. B. Yu, D. Q. Zhang, H. Liu, X. Han, Multimodal ellipsoid model for non-probabilistic structural uncertainty quantification and propagation, Int. J. Mech. Mater. Des., 17 (2021), 633–657. https://doi.org/10.1007/s10999-021-09551-z doi: 10.1007/s10999-021-09551-z
|
[88]
|
Z. Kang, W. B. Zhang, Construction and application of an ellipsoidal convex model using a semi-definite programming formulation from measured data, Comput. Meth. Appl. Mech. Eng., 300 (2016), 461–489. https://doi.org/10.1016/j.cma.2015.11.025 doi: 10.1016/j.cma.2015.11.025
|
[89]
|
L. Wang, J. X. Liu, Dynamic uncertainty quantification and risk prediction based on the grey mathematics and outcrossing theory, Appl. Sci.-Basel, 12 (2022), 5389. https://doi.org/10.3390/app12115389 doi: 10.3390/app12115389
|
[90]
|
Y. H. Yan, X. J. Wang, Y. L. Li, Non-probabilistic credible set model for structural uncertainty quantification, Structures, 53 (2023), 1408–1424. https://doi.org/10.1016/j.istruc.2023.05.011 doi: 10.1016/j.istruc.2023.05.011
|
[91]
|
T. Zhang, J. Y. Jiao, J. Lin, H. Li, J. D. Hua, D. He, Uncertainty-based contrastive prototype-matching network towards cross-domain fault diagnosis with small data, Knowledge-Based Syst., 254 (2022), 109651. https://doi.org/10.1016/j.knosys.2022.109651 doi: 10.1016/j.knosys.2022.109651
|
[92]
|
J. Chen, D. Zhou, Z. Guo, J. Lin, C. Lyu, C. Lu, An active learning method based on uncertainty and complexity for gearbox fault diagnosis, IEEE Access, 7 (2019), 9022–9031. https://doi.org/10.1109/ACCESS.2019.2890979 doi: 10.1109/ACCESS.2019.2890979
|
[93]
|
H. Ma, C. Ekanayake, T. K. Saha, Power transformer fault diagnosis under measurement originated uncertainties, IEEE Trns. Dielectr. Electr. Insul., 19 (2012), 1982–1990. https://doi.org/10.1109/tdei.2012.6396956 doi: 10.1109/tdei.2012.6396956
|
[94]
|
X. J. Shi, H. B. Gu, B. Yao, Fuzzy Bayesian network fault diagnosis method based on fault tree for coal mine drainage system, IEEE Sens. J., 24 (2024), 7537–7547. https://doi.org/10.1109/jsen.2024.3354415 doi: 10.1109/jsen.2024.3354415
|
[95]
|
R. X. Duan, Y. N. Lin, Y. N. Zeng, Fault diagnosis for complex systems based on reliability analysis and sensors data considering epistemic uncertainty, Eksploat. Niezawodn., 20 (2018), 558–566. https://doi.org/10.17531/ein.2018.4.7 doi: 10.17531/ein.2018.4.7
|
[96]
|
J. Wang, H. Peng, W. P. Yu, J. Ming, M. J. Pérez-Jiménez, C. Y. Tao, et al., Interval-valued fuzzy spiking neural p systems for fault diagnosis of power transmission networks, Eng. Appl. Artif. Intell., 82 (2019), 102–109. https://doi.org/10.1016/j.engappai.2019.03.014 doi: 10.1016/j.engappai.2019.03.014
|
[97]
|
A. Hoballah, D. E. A. Mansour, I. B. M. Taha, Hybrid grey wolf optimizer for transformer fault diagnosis using dissolved gases considering uncertainty in measurements, IEEE Access, 8 (2020), 139176–139187. https://doi.org/10.1109/access.2020.3012633 doi: 10.1109/access.2020.3012633
|
[98]
|
K. Zhou, J. Tang, Probabilistic gear fault diagnosis using Bayesian convolutional neural network, IFAC-PapersOnLine, 55 (2022), 795–799. https://doi.org/10.1016/j.ifacol.2022.11.279 doi: 10.1016/j.ifacol.2022.11.279
|
[99]
|
H. T. Zhou, W. H. Chen, L. S. Cheng, J. Liu, M. Xia, Trustworthy fault diagnosis with uncertainty estimation through evidential convolutional neural networks, IEEE Trans. Ind. Inform., 19 (2023), 10842–10852. https://doi.org/10.1109/TⅡ.2023.3241587 doi: 10.1109/TⅡ.2023.3241587
|
[100]
|
S. Huang, R. Duan, J. He, T. Feng, Y. Zeng, Fault diagnosis strategy for complex systems based on multi-source heterogeneous information under epistemic uncertainty, IEEE Access, 8 (2020), 50921–50933. https://doi.org/10.1109/ACCESS.2020.2980397 doi: 10.1109/ACCESS.2020.2980397
|
[101]
|
S. X. Liu, S. Y. Zhou, B. Y. Li, Z. H. Niu, M. Abdullah, R. R. Wang, Servo torque fault diagnosis implementation for heavy-legged robots using insufficient information, ISA Transactions, 147 (2024), 439–452. https://doi.org/10.1016/j.isatra.2024.02.004 doi: 10.1016/j.isatra.2024.02.004
|
[102]
|
T. Zhang, S. He, J. Chen, T. Pan, Z. Zhou, Toward small sample challenge in intelligent fault diagnosis: Attention-weighted multidepth feature fusion net with signals augmentation, IEEE Trans. Instrum. Meas., 71 (2022), 1–13. https://doi.org/10.1109/TIM.2021.3134999 doi: 10.1109/TIM.2021.3134999
|
[103]
|
A. Kulkarni, J. Terpenny, V. Prabhu, Sensor selection framework for designing fault diagnostics system, Sensors, 21 (2021), 6470. https://doi.org/10.3390/s21196470 doi: 10.3390/s21196470
|
[104]
|
C. Herrojo, F. Paredes, J. Mata-Contreras, F. Martín, Chipless-rfid: A review and recent developments, Sensors, 19 (2019), 3385. https://doi.org/10.3390/s19153385 doi: 10.3390/s19153385
|
[105]
|
T. Kalsoom, N. Ramzan, S. Ahmed, M. Ur-Rehman, Advances in sensor technologies in the era of smart factory and industry 4.0, Sensors, 20 (2020), 6783. https://doi.org/10.3390/s20236783 doi: 10.3390/s20236783
|
[106]
|
A. Leal, J. Casas, C. Marques, M. J. Pontes, A. Frizera, Application of additive layer manufacturing technique on the development of high sensitive fiber bragg grating temperature sensors, Sensors, 18 (2018), 4120. https://doi.org/10.3390/s18124120 doi: 10.3390/s18124120
|
[107]
|
G. D. Lewis, P. Merken, M. Vandewal, Enhanced accuracy of cmos smart temperature sensors by nonlinear curvature correction, Sensors, 18 (2018), 4087. https://doi.org/10.3390/s18124087 doi: 10.3390/s18124087
|
[108]
|
H. Landaluce, L. Arjona, A. Perallos, F. Falcone, I. Angulo, F. Muralter, A review of iot sensing applications and challenges using rfid and wireless sensor networks, Sensors, 20 (2020), 2495. https://doi.org/10.3390/s20092495 doi: 10.3390/s20092495
|
[109]
|
S. L. Wei, W. B. Qin, L. W. Han, F. Y. Cheng, The research on compensation algorithm of infrared temperature measurement based on intelligent sensors, Cluster Comput., 22 (2019), 6091–6100. https://doi.org/10.1007/s10586-018-1828-5 doi: 10.1007/s10586-018-1828-5
|
[110]
|
M. Tessarolo, L. Possanzini, E. G. Campari, R. Bonfiglioli, F. S. Violante, A. Bonfiglio, et al., Adaptable pressure textile sensors based on a conductive polymer, Flex. Print. Electron., 3 (2018), 034001. https://doi.org/10.1088/2058-8585/aacbee doi: 10.1088/2058-8585/aacbee
|
[111]
|
K. A. Mathias, S. M. Kulkarni, Investigation on influence of geometry on performance of a cavity-less pressure sensor, IOP Conf. Ser.: Mater. Sci. Eng., 417 (2018), 012035. https://doi.org/10.1088/1757-899x/417/1/012035 doi: 10.1088/1757-899x/417/1/012035
|
[112]
|
W. P. Eaton, J. H. Smith, Micromachined pressure sensors: Review and recent developments, Smart Mater. Struct., 6 (1997), 30–41. https://doi.org/10.1117/12.276606 doi: 10.1117/12.276606
|
[113]
|
M. Mousavi, M. Alzgool, S. Towfighian, A mems pressure sensor using electrostatic levitation, IEEE Sens. J., 21 (2021), 18601–18608. https://doi.org/10.1109/JSEN.2021.3091665 doi: 10.1109/JSEN.2021.3091665
|
[114]
|
A. P. Cherkun, G. V. Mishakov, A. V. Sharkov, E. I. Demikhov, The use of a piezoelectric force sensor in the magnetic force microscopy of thin permalloy films, Ultramicroscopy, 217 (2020), 113072. https://doi.org/10.1016/j.ultramic.2020.113072 doi: 10.1016/j.ultramic.2020.113072
|
[115]
|
A. Nastro, M. Ferrari, V. Ferrari, Double-actuator position-feedback mechanism for adjustable sensitivity in electrostatic-capacitive mems force sensors, Sens. Actuator A-Phys., 312 (2020), 112127. https://doi.org/10.1016/j.sna.2020.112127 doi: 10.1016/j.sna.2020.112127
|
[116]
|
M. L. Gödecke, C. M. Bett, D. Buchta, K. Frenner, W. Osten, Optical sensor design for fast and process-robust position measurements on small diffraction gratings, Opt. Lasers Eng., 134 (2020), 106267. https://doi.org/10.1016/j.optlaseng.2020.106267 doi: 10.1016/j.optlaseng.2020.106267
|
[117]
|
Y. J. Chan, A. R. Carr, S. Charkhabi, M. Furnish, A. M. Beierle, N. F. Reuel, Wireless position sensing and normalization of embedded resonant sensors using a resonator array, Sens. Actuator A-Phys., 303 (2020), 111853. https://doi.org/10.1016/j.sna.2020.111853 doi: 10.1016/j.sna.2020.111853
|
[118]
|
J. A. Kim, J. W. Kim, C. S. Kang, J. Y. Lee, J. Jin, On-machine calibration of angular position and runout of a precision rotation stage using two absolute position sensors, Measurement, 153 (2020), 107399. https://doi.org/10.1016/j.measurement.2019.107399 doi: 10.1016/j.measurement.2019.107399
|
[119]
|
L. E. Helseth, On the accuracy of an interdigital electrostatic position sensor, J. Electrost., 107 (2020), 103480. https://doi.org/10.1016/j.elstat.2020.103480 doi: 10.1016/j.elstat.2020.103480
|
[120]
|
K. Palmer, H. Kratz, H. Nguyen, G. Thornell, A highly integratable silicon thermal gas flow sensor, J. Micromech. Microeng., 22 (2012), 065015. https://doi.org/10.1088/0960-1317/22/6/065015 doi: 10.1088/0960-1317/22/6/065015
|
[121]
|
A. Moreno-Gomez, C. A. Perez-Ramirez, A. Dominguez-Gonzalez, M. Valtierra-Rodriguez, O. Chavez-Alegria, J. P. Amezquita-Sanchez, Sensors used in structural health monitoring, Arch. Comput. Method Eng., 25 (2018), 901–918. https://doi.org/10.1007/s11831-017-9217-4 doi: 10.1007/s11831-017-9217-4
|
[122]
|
A. M. Shkel, Smart mems: Micro-structures with error-suppression and self-calibration control capabilities, Proceedings of the 2001 American Control Conference, Arlington, VA, USA, 2001, 1208–1213. https://doi.org/10.1109/ACC.2001.945886
|
[123]
|
X. Insausti, M. Zárraga-Rodríguez, C. Nolasco-Ferencikova, J. Gutiérrez-Gutiérrez, In-network algorithm for passive sensors in structural health monitoring, IEEE Signal Process. Lett., 30 (2023), 952–956. https://doi.org/10.1109/lsp.2023.3298279 doi: 10.1109/lsp.2023.3298279
|
[124]
|
B. Jeon, J. S. Yoon, J. Um, S. H. Suh, The architecture development of industry 4.0 compliant smart machine tool system (smts), J. Intell. Manuf., 31 (2020), 1837–1859. https://doi.org/10.1007/s10845-020-01539-4 doi: 10.1007/s10845-020-01539-4
|
[125]
|
M. H. Zhu, J. Li, W. B. Wang, D. P. Chen, Self-detection and self-diagnosis methods for sensors in intelligent integrated sensing system, IEEE Sens. J., 21 (2021), 19247–19254. https://doi.org/10.1109/JSEN.2021.3090990 doi: 10.1109/JSEN.2021.3090990
|
[126]
|
J. Chen, P. Li, G. B. Song, Z. Ren, Y. Tan, Y. J. Zheng, Feedback control for structural health monitoring in a smart aggregate based sensor network, Int. J. Struct. Stab. Dyn., 18 (2017), 1850064. https://doi.org/10.1142/S0219455418500645 doi: 10.1142/S0219455418500645
|
[127]
|
C. Wang, Z. M. Peng, R. Liu, C. Chen, Research on multi-fault diagnosis method based on time domain features of vibration signals, Sensors, 22 (2022), 8164. https://doi.org/10.3390/s22218164 doi: 10.3390/s22218164
|
[128]
|
Z. F. Du, R. H. Zhang, H. Chen, Characteristic signal extracted from a continuous time signal on the aspect of frequency domain, Chin. Phys. B, 28 (2019), 090502. https://doi.org/10.1088/1674-1056/ab344a doi: 10.1088/1674-1056/ab344a
|
[129]
|
Y. Lu, J. Tang, On time-frequency domain feature extraction of wave signals for structural health monitoring, Measurement, 114 (2018), 51–59. https://doi.org/10.1016/j.measurement.2017.09.016 doi: 10.1016/j.measurement.2017.09.016
|
[130]
|
M. Imani, Modified pca, lda and lpp feature extraction methods for polsar image classification, Multimed. Tools Appl., 83 (2024), 41171–41192. https://doi.org/10.1007/s11042-023-17269-7 doi: 10.1007/s11042-023-17269-7
|
[131]
|
Z. Xia, Y. Chen, C. Xu, Multiview pca: A methodology of feature extraction and dimension reduction for high-order data, IEEE T. Cybern., 52 (2022), 11068–11080. https://doi.org/10.1109/TCYB.2021.3106485 doi: 10.1109/TCYB.2021.3106485
|
[132]
|
Y. Aliyari Ghassabeh, F. Rudzicz, H. A. Moghaddam, Fast incremental lda feature extraction, Pattern Recognit., 48 (2015), 1999–2012. https://doi.org/10.1016/j.patcog.2014.12.012 doi: 10.1016/j.patcog.2014.12.012
|
[133]
|
E. Parsaeimehr, M. Fartash, J. A. Torkestani, Improving feature extraction using a hybrid of cnn and lstm for entity identification, Neural Process. Lett., 55 (2023), 5979–5994. https://doi.org/10.1007/s11063-022-11122-y doi: 10.1007/s11063-022-11122-y
|
[134]
|
P. Wang, X. M. Zhang, Y. Hao, A method combining cnn and elm for feature extraction and classification of sar image, J. Sens., 2019 (2019), 6134610. https://doi.org/10.1155/2019/6134610 doi: 10.1155/2019/6134610
|
[135]
|
O. İrsoy, E. Alpaydın, Unsupervised feature extraction with autoencoder trees, Neurocomputing, 258 (2017), 63–73. https://doi.org/10.1016/j.neucom.2017.02.075 doi: 10.1016/j.neucom.2017.02.075
|
[136]
|
Y. Y. Wang, D. J. Song, W. T. Wang, S. X. Rao, X. Y. Wang, M. N. Wang, Self-supervised learning and semi-supervised learning for multi-sequence medical image classification, Neurocomputing, 513 (2022), 383–394. https://doi.org/10.1016/j.neucom.2022.09.097 doi: 10.1016/j.neucom.2022.09.097
|
[137]
|
W. X. Sun, J. Chen, J. Q. Li, Decision tree and pca-based fault diagnosis of rotating machinery, Mech. Syst. Signal Proc., 21 (2007), 1300–1317. https://doi.org/10.1016/j.ymssp.2006.06.010 doi: 10.1016/j.ymssp.2006.06.010
|
[138]
|
N. R. Sakthivel, V. Sugumaran, S. Babudevasenapati, Vibration based fault diagnosis of monoblock centrifugal pump using decision tree, Expert Syst. Appl., 37 (2010), 4040–4049. https://doi.org/10.1016/j.eswa.2009.10.002 doi: 10.1016/j.eswa.2009.10.002
|
[139]
|
Y. Y. Li, L. Y. Song, Q. C. Sun, H. Xu, X. G. Li, Z. J. Fang, et al., Rolling bearing fault diagnosis based on quantum ls-svm, EPJ Quantum Technol., 9 (2022), 18. https://doi.org/10.1140/epjqt/s40507-022-00137-y doi: 10.1140/epjqt/s40507-022-00137-y
|
[140]
|
A. H. Zhang, D. L. Yu, Z. Q. Zhang, Tlsca-svm fault diagnosis optimization method based on transfer learning, Processes, 10 (2022), 362. https://doi.org/10.3390/pr10020362 doi: 10.3390/pr10020362
|
[141]
|
T. Huang, Q. Zhang, X. A. Tang, S. Y. Zhao, X. N. Lu, A novel fault diagnosis method based on cnn and lstm and its application in fault diagnosis for complex systems, Artif. Intell. Rev., 55 (2022), 1289–1315. https://doi.org/10.1007/s10462-021-09993-z doi: 10.1007/s10462-021-09993-z
|
[142]
|
H. Fang, H. Liu, X. Wang, J. Deng, J. An, The method based on clustering for unknown failure diagnosis of rolling bearings, IEEE Trans. Instrum. Meas., 72 (2023), 1–8. https://doi.org/10.1109/TIM.2023.3251406 doi: 10.1109/TIM.2023.3251406
|
[143]
|
A. Rodríguez-Ramos, A. J. da Silva Neto, O. Llanes-Santiago, An approach to fault diagnosis with online detection of novel faults using fuzzy clustering tools, Expert Syst. Appl., 113 (2018), 200–212. https://doi.org/10.1016/j.eswa.2018.06.055 doi: 10.1016/j.eswa.2018.06.055
|
[144]
|
L. K. Chang, S. H. Wang, M. C. Tsai, Demagnetization fault diagnosis of a pmsm using auto-encoder and k-means clustering, Energies, 13 (2020), 4467. https://doi.org/10.3390/en13174467 doi: 10.3390/en13174467
|
[145]
|
J. Du, S. P. Wang, H. Y. Zhang, Layered clustering multi-fault diagnosis for hydraulic piston pump, Mech. Syst. Signal Proc., 36 (2013), 487–504. https://doi.org/10.1016/j.ymssp.2012.10.020 doi: 10.1016/j.ymssp.2012.10.020
|
[146]
|
Y. Y. Li, J. D. Wang, H. Y. Zhao, C. Wang, Q. Shao, Adaptive dbscan clustering and gasa optimization for underdetermined mixing matrix estimation in fault diagnosis of reciprocating compressors, Sensors, 24 (2024), 167. https://doi.org/10.3390/s24010167 doi: 10.3390/s24010167
|
[147]
|
C. X. Jian, K. J. Yang, Y. H. Ao, Industrial fault diagnosis based on active learning and semi-supervised learning using small training set, Eng. Appl. Artif. Intell., 104 (2021), 104365. https://doi.org/10.1016/j.engappai.2021.104365 doi: 10.1016/j.engappai.2021.104365
|
[148]
|
S. Zheng, J. Zhao, A self-adaptive temporal-spatial self-training algorithm for semisupervised fault diagnosis of industrial processes, IEEE Trans. Ind. Inform., 18 (2022), 6700–6711. https://doi.org/10.1109/TⅡ.2021.3120686 doi: 10.1109/TⅡ.2021.3120686
|
[149]
|
J. Y. Long, Y. B. Chen, Z. Yang, Y. W. Huang, C. Li, A novel self-training semi-supervised deep learning approach for machinery fault diagnosis, Int. J. Prod. Res., 61 (2023), 8238–8251. https://doi.org/10.1080/00207543.2022.2032860 doi: 10.1080/00207543.2022.2032860
|
[150]
|
K. Yu, H. Z. Han, Q. Fu, H. Ma, J. Zeng, Symmetric co-training based unsupervised domain adaptation approach for intelligent fault diagnosis of rolling bearing, Meas. Sci. Technol., 31 (2020), 115008. https://doi.org/10.1088/1361-6501/ab9841 doi: 10.1088/1361-6501/ab9841
|
[151]
|
L. Wang, D. F. Zhou, H. Tian, H. Zhang, W. Zhang, Parametric fault diagnosis of analog circuits based on a semi-supervised algorithm, Symmetry-Basel, 11 (2019), 228. https://doi.org/10.3390/sym11020228 doi: 10.3390/sym11020228
|
[152]
|
C. X. Jian, Y. H. Ao, Imbalanced fault diagnosis based on semi-supervised ensemble learning, J. Intell. Manuf., 34 (2023), 3143–3158. https://doi.org/10.1007/s10845-022-01985-2 doi: 10.1007/s10845-022-01985-2
|
[153]
|
X. Li, F. L. Zhang, Classification of multi-type bearing fault features based on semi-supervised generative adversarial network (gan), Meas. Sci. Technol., 35 (2024), 025107. https://doi.org/10.1088/1361-6501/ad068e doi: 10.1088/1361-6501/ad068e
|
[154]
|
L. Wang, H. Tian, H. Zhang, Soft fault diagnosis of analog circuits based on semi-supervised support vector machine, Analog Integr. Circuits Process., 108 (2021), 305–315. https://doi.org/10.1007/s10470-021-01851-w doi: 10.1007/s10470-021-01851-w
|
[155]
|
P. Xu, L. X. Fu, K. Xu, W. B. Sun, Q. Tan, Y. P. Zhang, et al., Investigation into maize seed disease identification based on deep learning and multi-source spectral information fusion techniques, J. Food Compos. Anal., 119 (2023), 105254. https://doi.org/10.1016/j.jfca.2023.105254 doi: 10.1016/j.jfca.2023.105254
|
[156]
|
P. F. Zhang, T. R. Li, Z. Yuan, C. Luo, G. Q. Wang, J. Liu, et al., A data-level fusion model for unsupervised attribute selection in multi-source homogeneous data, Inf. Fusion, 80 (2022), 87–103. https://doi.org/10.1016/j.inffus.2021.10.017 doi: 10.1016/j.inffus.2021.10.017
|
[157]
|
M. B. Song, Y. F. Zhi, M. D. An, W. Xu, G. H. Li, X. L. Wang, Centrifugal pump cavitation fault diagnosis based on feature-level multi-source information fusion, Processes, 12 (2024), 196. https://doi.org/10.3390/pr12010196 doi: 10.3390/pr12010196
|
[158]
|
L. L. Liu, X. Wan, J. Y. Li, W. X. Wang, Z. G. Gao, An improved entropy-weighted topsis method for decision-level fusion evaluation system of multi-source data, Sensors, 22 (2022), 6391. https://doi.org/10.3390/s22176391 doi: 10.3390/s22176391
|
[159]
|
Y. W. Liu, Y. Q. Cheng, Z. Z. Zhang, J. J. Wu, Multi-information fusion fault diagnosis based on knn and improved evidence theory, J. Vib. Eng. Technol., 10 (2022), 841–852. https://doi.org/10.1007/s42417-021-00413-8 doi: 10.1007/s42417-021-00413-8
|
[160]
|
J. Xu, Y. Sui, T. Dai, A Bayesian network inference approach for dynamic risk assessment using multisource-based information fusion in an interval type-2 fuzzy set environment, IEEE Trans. Fuzzy Syst., 32 (2024), 5702–5713. https://doi.org/10.1109/TFUZZ.2024.3425495 doi: 10.1109/TFUZZ.2024.3425495
|
[161]
|
Y. C. Jie, Y. Chen, X. S. Li, P. Yi, H. S. Tan, X. Q. Cheng, Fufusion: Fuzzy sets theory for infrared and visible image fusion, In: Pattern recognition and computer vision, Singapore: Springer, 2024,466–478. https://doi.org/10.1007/978-981-99-8432-9_37
|
[162]
|
F. Y. Xiao, Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy, Inf. Fusion, 46 (2019), 23–32. https://doi.org/10.1016/j.inffus.2018.04.003 doi: 10.1016/j.inffus.2018.04.003
|
[163]
|
G. Koliander, Y. El-Laham, P. M. Djuric, F. Hlawatsch, Fusion of probability density functions, Proceedings of the IEEE, 110 (2022), 404–453. https://doi.org/10.1109/jproc.2022.3154399 doi: 10.1109/jproc.2022.3154399
|
[164]
|
Y. J. Pan, R. Q. An, D. Z. Fu, Z. Y. Zheng, Z. H. Yang, Unsupervised fault detection with a decision fusion method based on Bayesian in the pumping unit, IEEE Sens. J., 21 (2021), 21829–21838. https://doi.org/10.1109/jsen.2021.3103520 doi: 10.1109/jsen.2021.3103520
|
[165]
|
K. V. Kumar, A. Sathish, Medical image fusion based on type-2 fuzzy sets with teaching learning based optimization, Multimed. Tools Appl., 83 (2024), 33235–33262. https://doi.org/10.1007/s11042-023-16859-9 doi: 10.1007/s11042-023-16859-9
|
[166]
|
P. F. Zhang, T. R. Li, G. Q. Wang, C. Luo, H. M. Chen, J. B. Zhang, et al., Multi-source information fusion based on rough set theory: A review, Inf. Fusion, 68 (2021), 85–117. https://doi.org/10.1016/j.inffus.2020.11.004 doi: 10.1016/j.inffus.2020.11.004
|
[167]
|
Y. S. Wang, M. Y. He, L. Sun, D. Wu, Y. Wang, X. L. Qing, Weighted adaptive kalman filtering-based diverse information fusion for hole edge crack monitoring, Mech. Syst. Signal Proc., 167 (2022), 108534. https://doi.org/10.1016/j.ymssp.2021.108534 doi: 10.1016/j.ymssp.2021.108534
|
[168]
|
N. Guenther, M. Schonlau, Support vector machines, Stata J., 16 (2016), 917–937. https://doi.org/10.1177/1536867x1601600407 doi: 10.1177/1536867x1601600407
|
[169]
|
P. Cunningham, S. J. Delany, K-nearest neighbour classifiers-a tutorial, ACM Comput. Surv., 54 (2021), 128. https://doi.org/10.1145/3459665 doi: 10.1145/3459665
|
[170]
|
Z. Liu, S. B. Zhong, Q. Liu, C. X. Xie, Y. Z. Dai, C. Peng, et al., Thyroid nodule recognition using a joint convolutional neural network with information fusion of ultrasound images and radiofrequency data, Eur. Radiol., 31 (2021), 5001–5011. https://doi.org/10.1007/s00330-020-07585-z doi: 10.1007/s00330-020-07585-z
|
[171]
|
A. Y. Chen, F. Wang, W. H. Liu, S. Chang, H. Wang, J. He, et al., Multi-information fusion neural networks for arrhythmia automatic detection, Comput. Meth. Programs Biomed., 193 (2020), 105479. https://doi.org/10.1016/j.cmpb.2020.105479 doi: 10.1016/j.cmpb.2020.105479
|