Patent ID: 11886311
Assignee: SOOCHOW UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G  Y | IPC G

Claim 3:
4. The fault diagnosis method for a rolling bearing under variable working conditions based on a convolutional neural network and transfer learning according to claim 1, wherein Step 4 comprises:
Step 4.1: calculating classification loss of the samples in the source domain in the training process, wherein a process of the calculation is shown by the following formula:, loss
       
     
     classifier
    
    ⁢
    
     (
     
      y
      ,
      X
     
     )
    
   
   =
   
    
     1
     n
    
    ⁢
    
     
      ∑
      
       i
       =
       0
      
      
       n
       -
       1
      
     
     
      J
      ⁢
        
      
       (
       
        
         y
         i
        
        ,
        
         F
         ⁡
         (
         
          x
          i
         
         )
        
       
       )
      
     
    
   
  
  ,
 

wherein X represents a set of all samples in the source domain, and y represents a set of actual labels of all the samples in the source domain; n is a quantity of samples that participate in training, yi is an actual label of an ith sample, and F(xi) is a predicted result of the ith sample by a neural network; and J(·,·) represents a cross entropy loss function, and is defined as follows:, J
    ⁡
    (
    
     p
     ,
     q
    
    )
   
   =
   
    -
    
     
      ∑
      
       i
       =
       0
      
      
       C
       -
       1
      
     
     
      
       p
       
        i
          
       
      
      ⁢
      log
      ⁢
        
      
       (
       
        q
        i
       
       )
      
     
    
   
  
  ,
 

wherein when i is an actual label of the sample, pi is equal to 1, or otherwise, pi is equal to 0; qi is a probability outputted after a softmax activation function; and C is a total quantity of label classes; and
Step 4.2: combining the conditional distribution distance and a label training prediction loss of the samples in the source domain to form a multi-scale high-level feature alignment target function to be optimized, wherein a formula of the target function is as follows:, l
      total
     
     =
     
     
      
       min
       ⁢
         
       
        
         l
         
          classifier
            
         
        
        (
        
         
          y
          s
         
         ,
         
          X
          s
         
        
        )
       
      
      +
      
       λ
       ⁢
       
        
         ∑
         
          i
          =
          0
         
         
          
           n
           sub
          
          -
          1
         
        
        
         d
         ⁡
         (
         
          
           
            g
            i
           
            
           (
           
            X
            s
           
           )
          
          ,
          
           
            g
            i
           
            
           (
           
            X
            t
           
           )
          
         
         )
        
       
      
     
    
   
  
  
   
    
     
      =
      
      
       
        
         min
         F
        
        
         1
         
          n
          s
         
        
        ⁢
        
         
          ∑
          
           i
           =
           0
          
          
           
            n
            s
           
           -
           1
          
         
         
          J
          ⁡
          (
          
           
            y
            i
            s
           
           ,
           
            F
            ⁢
              
            
             (
             
              x
              i
              s
             
             )
            
           
          
          )
         
        
       
       +
       
        λ
        ⁢
        
         
          ∑
          
           i
           =
           0
          
          
           
            n
            
             sub
             -
            
           
           ⁢
           1
          
         
         
          
           d
           H
          
          (
          
           
            
             g
             i
            
            (
            
             X
             s
            
            )
           
           ,
           
            
             g
             i
            
            (
            
             X
             t
            
            )
           
          
          )
         
        
       
      
     
      
     ,
    
   
  
 

wherein F(·) represents a model output function, g(·) represents an output of one substructure in the multi-scale feature extractor, J(·,·) represents the cross entropy loss function, λ>0 is a superparameter, nsub is equal to a quantity of substructures in the multi-scale feature extractor, and dH·,· is the conditional distribution distance; and the foregoing expression may be used to enable the network F(·) provided from training to accurately predict labels of samples from the target domain.