Production equipment predictive maintenance method and system based on multi-modal signals

The invention provides a production equipment predictive maintenance method and system based on a multi-modal signal. The method comprises the following steps: collecting multi-modal signal data to a database; making a data set of a training model through the data in the database; building a prediction model by using a convolutional neural network, wherein the prediction model comprises a data fusion layer, a feature extraction layer and a full-connection classification layer; training the model to a convergence state meeting a precision requirement, and completing an online reasoning task; reading a multi-modal signal to a prediction model, and storing a prediction model reasoning result into a database; and reading the multi-mode signal and the prediction result in the database, and providing a statistical analysis report and an early warning mail reminding function for a user. According to the invention, a micro-service architecture design is adopted, all services are completely decoupled, and the system i