MLPC-CNN: A multi-sensor vibration signal fault diagnosis method under less computing resources

This publication has 30 references indexed in Scilit:

• Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy
  Information Fusion, 2019
• Fault detection of rolling element bearings using optimal segmentation of vibrating signals
  Mechanical Systems and Signal Processing, 2019
• Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization
  Mechanical Systems and Signal Processing, 2018
• A Weighted Combination Method for Conflicting Evidence in Multi-Sensor Data Fusion
  Sensors, 2018
• Scattering transform and LSPTSVM based fault diagnosis of rotating machinery
  Mechanical Systems and Signal Processing, 2018
• A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines
  Neurocomputing, 2018
• A Novel Evidence Theory and Fuzzy Preference Approach-Based Multi-Sensor Data Fusion Technique for Fault Diagnosis
  Sensors, 2017
• An Improved Evidential-IOWA Sensor Data Fusion Approach in Fault Diagnosis
  Sensors, 2017
• A New Engine Fault Diagnosis Method Based on Multi-Sensor Data Fusion
  Applied Sciences, 2017
• Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data
  Mechanical Systems and Signal Processing, 2016