Machinery fault diagnosis based on a modified hybrid deep sparse autoencoder using a raw vibration time-series signal.
Syahril Ramadhan SaufiMuhammad Firdaus IshamZair Asrar Bin AhmadMuhammad Danial Bin Abu HasanPublished in: J. Ambient Intell. Humaniz. Comput. (2023)
Keyphrases
- fault diagnosis
- vibration signal
- non stationary
- fault detection
- neural network
- expert systems
- bp neural network
- condition monitoring
- operating conditions
- fuzzy logic
- monitoring and fault diagnosis
- power transformers
- analog circuits
- electronic equipment
- chemical process
- multiple faults
- high frequency
- fault identification
- rbf neural network
- rotating machinery
- fault detection and diagnosis
- gas turbine
- power plant
- frequency domain
- failure diagnosis
- industrial systems
- multi sensor information fusion
- data mining
- wavelet analysis
- frequency band
- low frequency
- artificial neural networks
- decision making
- artificial intelligence