The invention relates to the technical field of remote monitoring, and discloses a
coal unloader remote
monitoring system based on AI learning and a cloud platform, and the
system comprises an
impact force monitoring module, a vibration mode analysis module, an operation state evaluation module, a
health diagnosis module, and an intelligent early warning module. According to the method, through calculation of the magnitude, the direction, the
stress time and the stress frequency of the
impact force, fine-grained recognition of
coal flow
impact force distribution and fusion of vibration data and impact force data are enhanced, comprehensiveness and multi-dimensional data cross analysis of
fault recognition of the
coal unloader are improved, the abnormal misjudgment rate is reduced, intelligent calculation based on AI learning is achieved, and the
fault recognition accuracy of the coal unloader is improved. According to the invention, a
health assessment result can be dynamically adjusted, misjudgment caused by single-point abnormity is avoided, a
cloud data processing mode enables
remote management to be converted from data collection to intelligent early warning, the prediction capability of equipment operation abnormity is enabled to be more timely, remote monitoring is combined with intelligent learning, multi-level assessment is formed, and the accuracy and decision efficiency of
remote management are improved.