Machine equipment state monitoring system based on deep learning and voice recognition

The invention discloses a machine equipment state monitoring system based on deep learning and voice recognition. The system comprises a training data collection module used for collecting voice signals; a manual marking module used for marking the voice signals to form a sound sample library, wherein sound samples are sent to a preset neural network model for training after being subjected to pretreatment and feature extraction; a real-time data collection module used for collecting the voice signals and sending the signals to the trained neural network model; and a state recognition module used for being combined with artificial experience to comprehensively recognize and determine a running state of a machine via the voice signal and feeding back and outputting a result. According to the system, the running state of machine equipment can be monitored in real time, and meanwhile when the machine equipment is faulted or in a dangerous state, an alarm signal is emitted to notice an equipment keeper to maintai