Planetary gearbox fault diagnosis based on data-driven valued characteristic multigranulation model with incomplete diagnostic information
- 1 September 2018
- journal article
- research article
- Published by Elsevier BV in Journal of Sound and Vibration
- Vol. 429, 63-77
- https://doi.org/10.1016/j.jsv.2018.05.020
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (61673142, 51275136)
- University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province (2016034)
This publication has 33 references indexed in Scilit:
- Bayesian classifiers based on probability density estimation and their applications to simultaneous fault diagnosisInformation Sciences, 2014
- Crack level estimation approach for planetary gearbox based on simulation signal and GRAJournal of Sound and Vibration, 2012
- A Method for Rule Extraction Based on Granular Computing: Application in the Fault Diagnosis of a Helicopter Transmission SystemJournal of Intelligent & Robotic Systems, 2012
- A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysisApplied Soft Computing, 2012
- Maintenance of approximations in incomplete ordered decision systems while attribute values coarsening or refiningKnowledge-Based Systems, 2012
- Fault detection of planetary gearboxes using new diagnostic parametersMeasurement Science and Technology, 2012
- Machine remaining useful life prediction: An integrated adaptive neuro-fuzzy and high-order particle filtering approachMechanical Systems and Signal Processing, 2012
- Rule Extracting based on MCG with its Application in Helicopter Power Train Fault DiagnosisJournal of Physics: Conference Series, 2011
- MGRS: A multi-granulation rough setInformation Sciences, 2010
- Rough sets theoryChemometrics and Intelligent Laboratory Systems, 1999