Patent ID: 11984135

Abstract:
System and method for offline embedded abnormal sound fault detection are disclosed, the system comprising a sound acquisition module, a sound audio feature extraction module, and a neural network module. The sound audio feature extraction module uses fast Fourier transform to process sample data in a frequency domain, and then inputs the sample data to the neural network module to complete anomaly classification. The neural network module comprises at least one CNN feature extraction layer, a long short-term memory (LSTM) layer, at least one fully connected and at least one classification layer, and a trigger decision layer. The number of network layers of the at least one CNN feature extraction layer is dynamically adjustable, a network structure of the at least one fully connected layer and the at least one classification layer is dynamically variable, and the trigger decision layer is configured to eliminate generalization errors generated by a neural network. The present disclosure works in an offline environment, has less dependence on the network, strong performance and reliable operation, and can adapt to changing working environment for anomaly diagnosis.