Jagath Sri Lal Senanayaka Research

Jagath Sri Lal Senanayaka Research

Thesis

• senanayakaOnlineConditionMonitoring2020 Naive Bayes Combiner

References

• A robust method for detection and classification of permanent magnet synchronous motor faults: Deep autoencoders and data fusion approach
  • Fusion with Bayes Classifier
  • Transfer learning
  • CWT
  • SAE
• Autoencoders and Data Fusion Based Hybrid Health Indicator for Detecting Bearing and Stator Winding Faults in Electric Motors
  • FFT
  • Autoencoder
  • Fusion FEI-DEO with Multiclass SVM
• Early detection and classification of bearing faults using support vector machine algorithm
  • SVM
• Fault detection and classification of permanent magnet synchronous motor in variable load and speed conditions using order tracking and machine learning
• Multiple Classifiers and Data Fusion for Robust Diagnosis of Gearbox Mixed Faults
  • MLP and CNN
  • Fusion Naive Bayes
• Toward Self-Supervised Feature Learning for Online Diagnosis of Multiple Faults in Electric Powertrains
  • Unsupervised with SVM
  • Supervised with CNN
• Towards online bearing fault detection using envelope analysis of vibration signal and decision tree classification algorithm
  • Envelope detection
  • Decision tree