Patent ID: 11875500
Assignee: WUHAN UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 8:
9. The failure diagnosis method as claimed in claim 8, wherein training the model of the network in Step 6 comprises:
dividing fused vibration images of respective failure states and corresponding codes of the failure states into a training set and a test set based on a ratio of 4:1, wherein a dimensionality of data for forward propagation to the Mlpc layer is set as n, n partial feature maps are output, n pooling kernels, whose sizes and step lengths are same as the feature maps, are adopted, averages of the pooling kernels are obtained to be input to the classifier, and an expression formula is:, S
    ap
    l
   
   =
   
    
     1
     c
    
    ⁢
    
     
      ∑
      
       i
       =
       1
      
      c
     
      
     
      X
      
       
        1
        :
        h
       
       ,
       
        1
        :
        w
       
       ,
       i
      
      l
     
    
   
  
  ,
 

wherein a term on the left represents a global average pooling result of an lth layer, a term X represents a range of a feature output map corresponding to the pooling kernel, and h and w represent values of the range,
an error between an actually output vector Y and a predicted vector H is obtained, l is set as a total number of classifications, a square error is adopted as an error function to determine an error of a network calculation result, a mathematical expression is as follows:, E
   =
   
    
     
      1
      2
     
     ⁢
     
      
       ∑
       
        n
        =
        1
       
       l
      
       
      
       
        (
        
         
          H
          n
         
         -
         
          Y
          n
         
        
        )
       
       2
      
     
    
    =
    
     
      1
      2
     
     ⁢
     
      
       
       
        
         H
         n
        
        -
        
         Y
         n
        
       
       
      
      2
     
    
   
  
  ,
 

wherein n represents an nth dimension of data, Hn represents the nth dimension of predicted data, Yn represents the nth dimension of actual data obtained through mapping after x data is input, and a gradient is obtained for a convolutional layer, the gradient being substantially a partial derivative of a mean difference function to a parameter weight and a bias number, as represented in the following:, ∂
     E
    
    
     ∂
     
      K
      ij
     
    
   
   =
   
    
     ∑
     
      U
      ,
      v
     
    
    
     
      (
      
       δ
       j
       n
      
      )
     
     ⁢
     
      uv
      ⁡
      (
      
       S
       i
       
        n
        -
        1
       
      
      )
     
     ⁢
     uv
    
   
  
  ,
 

wherein S represents an area of a feature map and a convolution operator calculation, n represents an nth pooling layer, i represents an ith feature map of the nth pooling layer, and, based on a weight shared principle of a CNN network, associated gradients are added up for a sum and a gradient formula is obtained:, ∂
     E
    
    
     ∂
     
      b
      j
     
    
   
   =
   
    
     ∑
     
      U
      ,
      v
     
    
    
     
      (
      
       δ
       j
       k
      
      )
     
     ⁢
     uv
    
   
  
  ,
 

wherein the calculation is continuously optimized based on a stochastic gradient descend method to obtain a gradient of each layer, an adaptive parameter (W, b) in the network is updated to optimize performance of the model, and an updated mathematical model is as follows:, {
  
   
    
     
      
       
        k
        ij
        l
       
       =
       
        
         k
         ij
         l
        
        -
        
         β
         ⁢
         
          
           ∂
           E
          
          
           ∂
           
            k
            ij
            l
           
          
         
        
       
      
     
    
    
     
      
       
        b
        j
        l
       
       =
       
        
         b
         j
         l
        
        -
        
         β
         ⁢
         
          
           ∂
           E
          
          
           ∂
           
            b
            j
            l
           
          
         
        
       
      
     
    
   
   .