Data of vibration sensor of metro electromechanical equipment
The abnormality of subway mechanical and electrical equipment is an important topic that needs to be concerned about at present. When the abnormality is found, it is necessary to find the cause of the abnormality in time to prevent great maintenance loss. Testing the abnormal vibration of the equipment has a great role in improving the work efficiency and improving the service life of the equipment. Current maintenance frequency of subway equipment system is higher, fault or abnormal samples are scarce, how to find the equipment is also the difficulty, therefore, 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 two kinds of scenarios, parta and parts, parta is always shaking the sensor, partb is in a collection cycle, a period of time shaking the sensor and the rest of the time not shaking, and then the data collected, the data is from August 2022 to October 2022.