A new fault diagnosis method based on component-wise expectation-maximization algorithm and K-means algorithm
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- Type
- article
- Published
- 2017-05-01
- Cited by
- 3
- References
- 26
- OpenAlex
- https://openalex.org/W2763102960
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:34960155
Keywords
Expectation–maximization algorithm, Algorithm, Component (thermodynamics), Mixture model, Computer science
References
- A Component-Wise EM Algorithm for Mixtures
- Finite Mixture Models
- Dynamic neural network-based estimator for fault diagnosis in reaction wheel actuator of satellite attitude control system
- Survey on data-driven industrial process monitoring and diagnosis
- A k-means clustering algorithm
- Fault Diagnosis for Satellite Attitude Control Systems With Four Flywheels
- An Improved Detection Statistic for Monitoring the Nonstationary and Nonlinear Processes
- A nonlinear kernel Gaussian mixture model based inferential monitoring approach for fault detection and diagnosis of chemical processes
- Low-frequency Periodic Error Identification and Compensation for Star Tracker Attitude Measurement
- Fault diagnosis of machines via parameter estimation and knowledge processing - Tutorial paper
- NASA's integrated space transportation plan
- Regularized robust filter for attitude determination system with relative installation error of star trackers
- Machine fault diagnosis based on Gaussian mixture model and its application
- A unified framework for contrast research of the latent variable multivariate regression methods
- Fault Diagnosis of an Actuator in the Attitude Control Subsystem of a Satellite using Neural Networks
- Failure-Space: A systems engineering look at 50 space system failures
- Model-based Robust Fault Diagnosis for Satellite Control Systems Using Learning and Sliding Mode Approaches
- Fault Detection and Diagnosis in Industrial Systems
- Notice of RetractionGaussian Research of Turbine Faults Diagnosis Base on Mixture Models
- Fault Diagnosis of Satellite Attitude Control System Based on Multi-variable Statistical Technology
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