Identification method of abnormal working condition of equipment based on big data operation

In order to reduce the frequency of equipment failure during operation, the decision tree algorithm is introduced to carry out research on the design of recognizing abnormal working conditions of equipment operation based on big data. Sensors, instruments and monitoring systems are used to perceive the operating conditions of equipment, and based on this, the collection and pre-processing of equipment operating data are realized: data in different formats and from different sources are converted and integrated to realize metadata feature extraction and category classification based on the decision tree; spatial clustering is performed on data of the same category to realize data clustering and anomaly data identification. Anomaly identification is important for reducing the frequency of equipment failure.