Machinery Early Fault Detection Based on Dirichlet Process Mixture Model
The most commonly used single feature-based anomaly detection method for the complex machinery, such as large wind power equipment, steam turbine generator sets, and reciprocating compressors, exhibits a defect of low-alarm accuracy due to the non-stationary characteristic of the vibration signals....
Main Authors: | Bo Ma, Yi Zhao, Ying Zhang, Qing Lei Jiang, Xiu Qun Hou |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8756102/ |
Similar Items
-
Kernel Analysis Based on Dirichlet Processes Mixture Models
by: Jinkai Tian, et al.
Published: (2019-09-01) -
A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
by: Yu Wei, et al.
Published: (2019-04-01) -
Research on an ID-PCA Early Fault Detection Method for Rolling Bearings
by: Jin Guo, et al.
Published: (2022-04-01) -
Fault Diagnosis of Rotating Machinery Based on Multi-Sensor Signals and Median Filter Second-Order Blind Identification (MF-SOBI)
by: Feng Miao, et al.
Published: (2020-05-01) -
Positive Sequential Data Modeling Using Continuous Hidden Markov Models Based on Inverted Dirichlet Mixtures
by: Ru Wang, et al.
Published: (2019-01-01)