Vibration sensor data acquisition and analysis for metro equipment

This paper adopts the method of artificial simulation equipment abnormal, collected the electrical equipment abnormal data, and puts forward the analysis of equipment abnormal data, the model can provide the premise for real-time automatic detection of anomalies, the method is proposed, can provide the electrical equipment abnormal perception data set, algorithm, model, greatly reduce the frequent maintenance frequency, provide technical solutions for automation operations and remote operations. This paper provides a major dataset of metro device vibration sensors to build an efficient machine learning-based analytical model that can detect device anomalies. The data set includes collected more perceptual signals, including speed, acceleration, frequency and other 15 variables. the data is from August 2022 to August 2023