The equipment status monitoring system utilizes multi-dimensional collection methods and advanced technologies such as IoT and big data to construct a standard digital model of the equipment. At the same time, it integrates AI algorithm models such as mechanism models, intelligent algorithms and machine learning to achieve intelligent faults warning, diagnosis, and life prediction of equipment, and achieve full lifecycle management of equipment. The platform is equipped with self-learning mechanism, which can automatically iterate and upgrade algorithm models, continuously optimize the accuracy of equipment models, intelligent warning and diagnostic models, gradually form an industry replicable expert knowledge base, and improve the standardization and specialization level of industry equipment operation and maintenance management.