Incipient fault diagnosis based on moving probabilistic neural network

A new probability density tracing analysis method named moving probability neural networks (MPNN) is proposed to identify the incipient fault of electromechanical equipment. The MPNN is a three-layer network: the first layer consists of invariable sample set, dynamic signal is moved into the second layer, and condition probability density estimation of sample set is the output of the third layer. So the probability density of signal is continuously projected to the uniform sample set. The recursive algorithm is achieved by dividing the network into subnets. Using the attenuation characteristic of Gaussian function or the piecewise-linear approximation for Gaussian function, the computational load of MPNN is reduced further. Finally, the surge process data of centrifugal compressor is analyzed via this network to verify the effectiveness for diagnosis of the incipient faults.