The invention discloses a voiceprint-based 10 kV
circuit breaker mechanical fault detection method. Comprising the following steps: S1, building a MobileNetYOLOv4 neural
network model; s2, acquiring voiceprints through a high-sensitivity sensor; s3, performing
convolution operation on a
convolution kernel of edge side identification early warning; s4,
circuit breaker fault identification; s5, carrying out improved S transformation on the
signal; according to the invention, task allocation is dynamically calculated by a
resource allocation module embedded in a cloud platform, a lightweight model is constructed, the model operation speed is accelerated, and a real-time target identification and classification
system is constructed; the Moblienet neural
network structure reduces the running memory of a neural
network model and accelerates the running speed of the neural
network model, and in order to improve the target recognition speed and solidify the model to an
edge device, the MobileNet
network structure applies a deep separable
convolution technology, so that the target recognition efficiency is improved. The standard convolution of the
convolutional neural network is divided into two steps of deep convolution and point-by-point convolution in the calculation process, so that the calculation amount of the network is greatly reduced.