The invention provides a building electromechanical
system fault intelligent prediction method and
system, and the method comprises the steps: step 1, building a BIM model of a building; step 2, adopting the technology of
the Internet of Things to monitor important electromechanical equipment in real time, dynamically collecting
monitoring data, wherein the important electromechanical equipment iscentral and subarea control and
power equipment of an electromechanical
system; step 3, collecting repair work order information, performing semantic recognition on each repair work order, and matching with the BIM information to determine a repair space and an associated electromechanical system or device; step 4, establishing an electromechanical equipment fault prediction model by adopting
principal component analysis and a neural network
algorithm, and performing
machine learning; step 5, using a
cross validation method for network training until the accuracy of the obtained
artificial neuron network model is available; and step 6, applying the
artificial neuron network model to perform fault prediction, and notifying maintenance personnel of potential faults to perform key inspection. According to the scheme of the invention, the method can achieve the accurate prediction of the faults of the building electromechanical equipment, reduce the sudden faults of the building electromechanical equipment by 20%, guarantee the stable operation of a large public building, and reduce operation and maintenance cost.