Litcius/Paper detail

Unsupervised fault detection with multi-source anomaly sensitivity enhancing convolutional autoencoder for high-speed train bogie bearings

Zhixuan Li, Kai Zhang, Qing Zheng, Guofu Ding, Wei Hao, Haizhu Zhang, Weihua Zhang

2025Expert Systems with Applications18 citationsDOI

Topics & Concepts

AutoencoderBogieComputer scienceSensitivity (control systems)Anomaly detectionArtificial intelligencePattern recognition (psychology)Convolutional neural networkFault (geology)Fault detection and isolationReal-time computingDeep learningMachine learningGeologySeismologyElectronic engineeringEngineeringStructural engineeringActuatorMachine Fault Diagnosis TechniquesFault Detection and Control SystemsAnomaly Detection Techniques and Applications
Unsupervised fault detection with multi-source anomaly sensitivity enhancing convolutional autoencoder for high-speed train bogie bearings | Litcius