Real-time and energy-efficient bearing fault diagnosis using discriminative wavelet-based fault features on a multi-core system.
Jaeyoung KimMyeongsu KangIn-Kyu JeongHeesung JunJong-Myon KimByeong-Keun ChoiPublished in: ICPHM (2015)
Keyphrases
- fault diagnosis
- energy efficient
- real time
- monitoring and fault diagnosis
- fault detection
- neural network
- fuzzy logic
- expert systems
- multiple faults
- wireless sensor networks
- feature extraction
- power transformers
- fault detection and diagnosis
- electronic equipment
- gas turbine
- sensor networks
- operating conditions
- rotating machinery
- energy consumption
- multi core architecture
- condition monitoring
- chemical process
- fault diagnostic
- analog circuits
- extracted features
- fault detection and isolation
- control system
- multiresolution
- energy efficiency
- cost effective
- electrical power systems
- power plant
- fault isolation
- base station
- fault tree
- quality of service
- tennessee eastman
- computational intelligence