Anomaly Detection During Durability Testing of Rear-Wheel Steering Using LSTM Autoencoder

This publication has 24 references indexed in Scilit:

• A sound-vibration physical-information fusion constraint-guided deep learning method for rolling bearing fault diagnosis
  Reliability Engineering & System Safety, 2024
• Vibration data-driven anomaly detection in UAVs: A deep learning approach
  Engineering Science and Technology, an International Journal, 2024
• A novel unsupervised deep learning approach for vibration-based damage diagnosis using a multi-head self-attention LSTM autoencoder
  Measurement, 2024
• Sensor-aware CapsNet: Towards trustworthy multisensory fusion for remaining useful life prediction
  Journal of Manufacturing Systems, 2023
• A novel bearing fault diagnosis approach using the Gaussian mixture model and the weighted principal component analysis
  Reliability Engineering & System Safety, 2023
• FGDAE: A new machinery anomaly detection method towards complex operating conditions
  Reliability Engineering & System Safety, 2023
• Recognizing multivariate geochemical anomalies for mineral exploration by combining deep learning and one-class support vector machine
  Computers & Geosciences, 2020
• Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations
  Journal of Intelligent Manufacturing, 2019
• Detection and identification of windmill bearing faults using a one-class support vector machine (SVM)
  Measurement, 2019
• Effects of extreme pressure additive chemistry on rolling element bearing surface durability
  Tribology International, 2007