The invention relates to the technical field of robots, and particularly discloses a multi-joint
robot fault diagnosis
system and method based on a fractional order neural network, the fractional order neural network is adopted, and through the synergistic effect of a fractional order
convolution layer, a fractional order
pooling layer and a fractional order ReLU function, compared with an integer order model, the fault diagnosis efficiency of the multi-joint
robot is improved. According to the method, the non-linear and non-stationary feature capture capability of multi-joint vibration signals is remarkably enhanced, early weak fault features can be accurately extracted, the diagnosis accuracy of early faults such as
gear tooth micro
pitting corrosion and bearing clearance abnormity is improved, and the weak
fault recognition capability is enhanced; a temporary
decision maker mechanism under a distributed group learning framework retains the
advantage of low communication cost, and meanwhile, through joint
loss function optimization and dynamic
weight distribution,
data privacy protection and model training efficiency are considered, the communication cost is reduced, the
training period is shortened, and the
system performance is improved. The core requirements of industrial scenes for high precision, low time
delay and strong privacy of fault diagnosis are met.