A Fault Diagnosis and Prediction System for Rotating Machinery Based on Self-Attention Encoding and Decoding Structure
Due to harsh operating conditions and high load requirements,rotating machinery faults can lead to high maintenance costs and un-necessary downtime.It is necessary to develop an efficient and accurate rotating machinery fault online diagnosis and prediction system to help enterprises quickly identify faults,predict future events,and optimize maintenance plans.The construction state matrix represents the vibra-tion signal generated by rotating machinery,that is,a continuous time series is divided into several Windows,and each window is converted into an image.Image features are extracted and processed by a series codec of a specific structure to classify vibration patterns in a training da-ta set.The reliability and effectiveness of the online fault diagnosis and prediction system for rotating machinery based on self-attention codec structure are verified by simulation experiments.The state characteristic database of rotating machinery can accurately diagnose and predict the faults of rotating machinery.The system can help businesses optimize maintenance schedules and reduce downtime and maintenance costs.
万方数据
维普
