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VIBEX: an expert system for vibration fault diagnosis of rotating machinery using decision tree and decision table
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“The C4.5-induced decision tree has been introduced into the fault diagnosis of rolling element bearings [82,301,302], gearboxes [303], rotor systems [304], and centrifugal pumps [305].”
From Applications of machine learning to machine fault diagnosis: A review and roadmap (2020) · cites this paper as [82]
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Items 101–137 of 137
An Expert Fault Diagnosis System For Auto Wire Bond Machine
Tan, C. F. (2012) · Jurnal Teknologi · doi:10.11113/jt.v47.254 · Cited by 2
Fault Diagnosis of Induction Motors using Decision Trees
Tran, V. T., Yang, B., & Oh, M.-S. (2006) · University of Huddersfield Repository (University of Huddersfield) · Cited by 2
Self-citationDevelopment of Signal Recognition Technology for Automation Rule Based Diagnosis
Lee, J.-J., Park, D.-H., Kim, H.-J., et al. (2022) · Transactions of the Korean Society for Noise and Vibration Engineering · doi:10.5050/ksnve.2022.32.4.361 · Cited by 2
Smart condition monitoring by intergration of vibration, oil and wear particle analysis
Ebersbach, S., Peng, Z., & Kessissoglou, N. (2007) · ResearchOnline - JCU (James Cook University) · Cited by 2
Research of Pathology Expert System Based on Artificial Neural Network
Song, K., & Hui-ping, N. (2009) · International Forum on Computer Science-Technology and Applications · doi:10.1109/ifcsta.2009.283 · Cited by 2
Enhanced Helicopter Vibration Prediction With Hybrid Sampling and Cost Mining Techniques
Kim, J., Choi, K., & Yoo, D. (2025) · IEEE Access · doi:10.1109/access.2025.3583905 · Cited by 1
Data-driven fault diagnosis for multiple subsystems of deep-sea submersible
Zhao, Z., Fang, X., Zhang, C., et al. (2025) · Proceedings of the Institution of Mechanical Engineers Part M Journal of Engineering for the Maritime Environment · doi:10.1177/14750902251391865 · Cited by 1
“6,7 Moreover, these conventional methods may not scale well when processing large volumes of high-dimensional data, limiting their applicability in real-time operations.”
Difficult intubation assessment using statistical factor analysis decision tree
Wang, H.-C., Chen, W.-H., Tseng, C.-C., et al. (2013) · Journal of the Chinese Institute of Engineers · doi:10.1080/02533839.2013.815015 · Cited by 1
A Virtual Reality Based Diagnosis Scheme for Hydro Power Facility
Ai, Y., & Li, Z. (2012) · Asia-Pacific Power and Energy Engineering Conference (APPEEC) · doi:10.1109/appeec.2012.6307012 · Cited by 1
Diagnosis Rule Discovery Based on Causality in the Context of Fault Diagnosis in Rotating Machinery
Zhou, J., Wang, R. J., & Zhang, J. (2014) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.532.113 · Cited by 1
“Data-driven methods such as Neural Network [1], Rough Set [2], Expert System [3], Principal Component Analysis [4], Support Vector Machines [5], have some advantages over model-based methods under the condition of complex systems.”
MethodComparison of Data Mining and Neural Network Methods on Aero-engine Vibration Fault Diagnosis
Jiang, D., Xiong, K., Ding, Y., et al. (2007) · International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) · doi:10.1109/fskd.2007.224 · Cited by 1
“it is necessary to identify for possible causes and adopt remedial measures immediately when a fault occurs [1].”
BackgroundThe Research on the Framework of Machine Fault Diagnosis in Intelligent Manufacturing
Ji, M. (2018) · Lecture notes in electrical engineering · doi:10.1007/978-981-13-2375-1_63 · Cited by 1
Afanasyex, A. N., Voit, N. N., & Kanev, D. S. (2017) · IEEE International Conference on Application of Information and Communication Technologies (AICT) · doi:10.1109/icaict.2017.8687277 · Cited by 1
Cloud-Based Intelligent DSS Design for Emergency Professionals
Miah, S. J. (2012) · IGI Global eBooks · doi:10.4018/978-1-4666-2455-9.ch050 · Cited by 1
Using neural networks to predict road roughness
Soleimani, A., & Sahebi, A. A. (2012) · Cited by 1
Machine learning techniques for fault detection of induction motor bearings
Elango, M., Annamalai, A., & Venkateswaran, A. (2020) · AIP conference proceedings · doi:10.1063/5.0034204 · Cited by 1
Deep Learning Based Diagnostics of Orbit Patterns in Rotating Machinery
Jeong, H., Woo, S., Kim, S., Park, S., Kim, H., & Lee, S. (2016) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2016.v8i1.2582 · Cited by 3
A Steam Turbine Fault Diagnostic System Based on the Web and Database Technologies
Song, G. X., Gu, Y. J., He, Y. Y., et al. (2009) · Key engineering materials · doi:10.4028/www.scientific.net/kem.413-414.539
Prieto-Moreno, A., Llanes‐Santiago, O., Milanés-Cruz, A., et al. (2012) · Ingeniare. Revista chilena de ingeniería · doi:10.4067/s0718-33052012000100006
“También se puede encontrar su aplicación combinado con otras técnicas como se presentan en [37, 38].”
BackgroundVibration Fault Diagnosis of Rotating Machinery Using PSO-Based Radial Basis Function Network
Sun, H. C., Huang, C. M., Huang, Y. C., & Chen, H. F. (2013) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.302.474
“The expert system technique [1]-[4] combines an inference engine with a range of knowledge to develop guidelines for automatic diagnosis of faults in STGS.”
MethodA Case Study for a Turbogenerator Accident Using Multiscale Association
Yu, D., Wang, W., Zhang, Z., et al. (2008) · Journal of Engineering for Gas Turbines and Power · doi:10.1115/1.2943149
Comparison of RBF Neural Network and Support Vector Machine on Aero-Engine Vibration Fault Diagnosis
Xiong, K., Jiang, D. X., Ding, Y. S., et al. (2007) · Key engineering materials · doi:10.4028/www.scientific.net/kem.347.323
Application of Improved Genetic Algorithm in Maintenance Decision for Turbine-Generator Unit
He, Q., & Zhang, J. D. (2010) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.44-47.2940
Forecast system of pear scab management based on case-based reasoning and fuzzy ISODATA clustering
Li, L., Li-na, Z., & Li-wu, Z. (2012) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2012.6244272
The Application of Neural Systems in Vibrodiagnosis
Romaniuk, T. (2009) · Schedae Informaticae · doi:10.2478/v10149-010-0002-5
Fault diagnosis of turbo-generator based on support vector machine and genetic algorithm
Xiao-feng, S., Shen, Y. M., & Guo, L. (2009) · doi:10.1109/cccm.2009.5268111
“However, artificial neural networks have inherent drawbacks, such as local optimization solution, lack generalization, and uncontrolled convergence[2] [3].”
BackgroundPrieto-Moreno, A., Llanes‐Santiago, O., Milanés-Cruz, A., et al. (2012) · DOAJ (DOAJ: Directory of Open Access Journals)
A Partition-based Attribute Reduction Data Mining Approach for Multi-dimensional Datasets
Li, X., Qiu, G., & Lu, A. (2017) · International Journal of Signal Processing Image Processing and Pattern Recognition · doi:10.14257/ijsip.2017.10.12.05
Expert systems for monitoring, control and diagnosis of the CO2capture process system
Chan, C. W., Zhou, Q., & Tontiwachwuthikul, P. P. (2013) · doi:10.2217/ebo.13.356
Afanasyev, A. N., Voit, N. N., Kanev, D. S., et al. (2018) · Russian-Pacific Conference on Computer Technology and Applications (RPC) · doi:10.1109/rpc.2018.8482188
“EMYCIN [22], ESMDA [23], VIBEX [24] are representatives of expert systems.”
BackgroundA Vibration Fault Diagnosis System of HGS Based on FNN
Yu, W., Wang, Y., Gui, W., et al. (2007) · Elsevier eBooks · doi:10.1016/b978-008044485-7/50043-9
Fault Diagnosis of Coal Mining Machinery Based on State Parameters
Song, D. S. (2020) · Frontiers in artificial intelligence and applications · doi:10.3233/faia200741
Implementation of classifiers for choosing insurance policy using decision trees
HuangChin-Sheng, LinYu-Ju, & LinChe-Chern (2008) · WSEAS Transactions on Computers archive · doi:10.5555/1486693.1486708
Ball Bearing Monitoring Using Decision-Tree and Adaptive Neuro-Fuzzy Inference System
Euldji, R., Euldji, R., Boumahdi, M., et al. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-030-96311-8_23
Development of Rule-based Diagnosis for the Automatic Diagnosis of Vertical Pumps
Kang, M., Cheong, D.-Y., Park, D.-H., et al. (2024) · Transactions of the Korean Society for Noise and Vibration Engineering · doi:10.5050/ksnve.2024.34.6.717
Ji, M., Chen, L., Yao, Y., et al. (2025) · Shock and Vibration · doi:10.1155/vib/5237376
“Commonly used algorithms include expert systems [15, 16], the K-nearest neighbor (KNN) algorithm [17, 18], the decision tree [19, 20], the support vector machine [21, 22], the hidden Markov model [23, 24], and others.”
Failure Monitoring for Firefighting Pump Based on Long Short-Term Memory
Doan, Q. N., Thi, H. L., Thu, T. N. T., & Thanh, P. N. (2025) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-031-90629-9_46
Metadata
- Authors
- YANG, B., LIM, D., & TAN, A.
- Affiliations
- Pukyong National University (KR)
- Hyosung Corporation (South Korea) (KR)
- Queensland University of Technology (AU)
- Corresponding author
- YANG, B
- ORCID
- TAN, A
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2005-05
- Volume
- 28
- Issue
- 4
- Pages
- 735-742
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W1551846121
- Cited by
- 137
- References
- 0
- Citations by year
- 2025: 8 · 2024: 7 · 2023: 1 · 2022: 5 · 2021: 9 · 2020: 6 · 2019: 1 · 2018: 6 · 2017: 5 · 2016: 6 · 2015: 6 · 2014: 7 · 2013: 5 · 2012: 19
- FWCI
- 17.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Expert Systems with Applications (publishedVersion)
- Faculty of Built Environment and Engineering
- Funders
- Seoul National University
- Pukyong National University
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Computational Techniques and Applications
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- vibration fault diagnosis, rotating machinery, expert systems, decision tree, decision tables, knowledge base acquisition, inductive reasoning, probabilistic diagnosis
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