Machine-tool condition monitoring with Gaussian mixture models-based dynamic probabilistic clustering.
Javier Diaz-RozoConcha BielzaPedro LarrañagaPublished in: Eng. Appl. Artif. Intell. (2020)
Keyphrases
- gaussian mixture model
- condition monitoring
- mixture model
- finite mixture models
- tool wear
- unsupervised learning
- minimum message length
- probabilistic model
- expectation maximization
- em algorithm
- finite mixtures
- density estimation
- fault detection
- vibration signal
- clustering algorithm
- k means
- bayesian information criterion
- cutting tool
- speaker recognition
- probability density
- feature vectors
- nuclear power plant
- fault diagnosis
- speaker identification
- model based clustering
- generative model
- bayesian networks
- acoustic emission
- self organizing maps
- mixture modeling
- covariance matrices
- model selection
- information retrieval
- pairwise