Lou et al., 2004
ViewPDF| Publication | Publication Date | Title |
|---|---|---|
| Lou et al. | Bearing fault diagnosis based on wavelet transform and fuzzy inference | |
| Lang et al. | Artificial intelligence-based technique for fault detection and diagnosis of EV motors: A review | |
| Hamadache et al. | A comprehensive review of artificial intelligence-based approaches for rolling element bearing PHM: Shallow and deep learning | |
| Principi et al. | Unsupervised electric motor fault detection by using deep autoencoders | |
| Sabir et al. | LSTM based bearing fault diagnosis of electrical machines using motor current signal | |
| Cheng et al. | Machine health monitoring using adaptive kernel spectral clustering and deep long short-term memory recurrent neural networks | |
| Abid et al. | Multidomain features-based GA optimized artificial immune system for bearing fault detection | |
| Zhang et al. | Feature selection for high-dimensional machinery fault diagnosis data using multiple models and radial basis function networks | |
| Maurya et al. | Condition monitoring of machines using fused features from EMD-based local energy with DNN | |
| Abed et al. | A robust bearing fault detection and diagnosis technique for brushless DC motors under non-stationary operating conditions | |
| Li et al. | Fault diagnosis of rotating machinery with a novel statistical feature extraction and evaluation method | |
| Barakat et al. | Parameter selection algorithm with self adaptive growing neural network classifier for diagnosis issues | |
| Kim et al. | An explainable neural network for fault diagnosis with a frequency activation map | |
| Sharma et al. | Novel ensemble techniques for classification of rolling element bearing faults | |
| Barakat et al. | Hard competitive growing neural network for the diagnosis of small bearing faults | |
| Anwarsha et al. | Recent advancements of signal processing and artificial intelligence in the fault detection of rolling element bearings: a review | |
| Haroun et al. | Feature selection for enhancement of bearing fault detection and diagnosis based on self-organizing map | |
| Almounajjed et al. | Condition monitoring and fault detection of induction motor based on wavelet denoising with ensemble learning | |
| Vitor et al. | Induction motor short circuit diagnosis and interpretation under voltage unbalance and load variation conditions | |
| Schmitt et al. | Detecting bearing faults in line-connected induction motors using information theory measures and neural networks | |
| Yang et al. | Automatic extraction of a health indicator from vibrational data by sparse autoencoders | |
| Tajik et al. | Gas turbine shaft unbalance fault detection by using vibration data and neural networks | |
| Barbosa et al. | Fault detection and classification in cantilever beams through vibration signal analysis and higher-order statistics | |
| Hassannejad et al. | Adaptive Wavelet-Based Physics-Informed CNN for Bearing Fault Diagnosis | |
| Jaumann et al. | Condition Monitoring using Convolutional Neural Network in Agricultural Machinery-Use Case: Disc Mower |