Detection of Emerging Faults on Industrial Gas Turbines Using Extended Gaussian Mixture Models
This paper extends traditional Gaussian mixture model (GMM) techniques to provide recognition of operational states and detection of emerging faults for industrial systems. A variational Bayesian method allows a GMM to cluster with its mixture components to facilitate the extraction of steady-state...
Saved in:
Main Authors: | Yu Zhang, Chris Bingham, Miguel Martínez-García, Darren Cox |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2017-01-01
|
Series: | International Journal of Rotating Machinery |
Online Access: | http://dx.doi.org/10.1155/2017/5435794 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Orthogonal Wavelet Transform-Based Gaussian Mixture Model for Bearing Fault Diagnosis
by: Weipeng Li, et al.
Published: (2023-01-01) -
Fault Detection and Diagnosis for Gas Turbines Based on a Kernelized Information Entropy Model
by: Weiying Wang, et al.
Published: (2014-01-01) -
An Extendable Gaussian Mixture Model for Lane-Based Queue Length Estimation Based on License Plate Recognition Data
by: Chaofeng Tan, et al.
Published: (2022-01-01) -
Damage Detection of Refractory Based on Principle Component Analysis and Gaussian Mixture Model
by: Changming Liu, et al.
Published: (2018-01-01) -
An Improved Gaussian Mixture CKF Algorithm under Non-Gaussian Observation Noise
by: Hongjian Wang, et al.
Published: (2016-01-01)