齿轮和齿轮箱作为机械设备常用的旋转机械设备，不仅可以传递更大的功率和载荷，而且具有更好的可靠性。但在高精度切割加工中，当齿轮在变速、变载等复杂条件下工作时，容易损坏、磨损、断齿，大大降低了加工精度。因此，齿轮的状态监测和故障诊断尤为重要。

在实验平台下验证本文提到的方法的有效性( 图2) ，采集试验台减速箱齿轮不同状态下的实时振动信号。齿轮减速箱型号为 JZQ采样频率为200 8 200 Hz。选择齿轮正常、不平衡、松动、脱位、断齿、裂纹 6 实验验证不同状态的数据。

function [pe hist] = pec(y,m,t)  %  Calculate the permutation entropy  %  Input:   y: time series; %           m: order of permuation entropy %           t: delay time of permuation entropy,   % Output:  %           pe:    permuation entropy %           hist:  the histogram for the order distribution    ly = length(y); permlist = perms(1:m); c(1:length(permlist))=0;       for j=1:ly-t*(m-1)      [a,iv]=sort(y(j:t:j t*(m-1)));      for jj=1:length(permlist)
         if (abs(permlist(jj,:)-iv))==0
             c(jj) = c(jj) + 1 ;
         end
     end
 end

hist = c;
 
c=c(find(c~=0));
p = c/sum(c);
pe = -sum(p .* log(p));

function outdata = PE( indata, delay, order, windowSize )
% @brief PE efficiently [1] computes values of permutation entropy [2] in 
% maximally overlapping sliding windows
%
% INPUT 
%   - indata - considered time series 
%   - delay - delay between points in ordinal patterns (delay = 1 means successive points)
%   - order - order of the ordinal patterns (order+1 - number of points in ordinal patterns)
%   - windowSize - size of sliding window
% OUTPUT 
%   - outdata - values of normalised permutation entropy as defined in [2]
%
% REFERENCES
% [1] Unakafova, V.A., Keller, K., 2013. Efficiently measuring complexity 
% on the basis of real-world data. Entropy, 15(10), 4392-4415.
% [2] Bandt C., Pompe B., 2002. Permutation entropy: a natural complexity 
% measure for time series. Physical review letters, APS
%
% @author Valentina Unakafova
% @date 10.11.2017
% @email UnakafovaValentina(at)gmail.com

 load( ['table' num2str( order ) '.mat'] ); % the precomputed table
 patternsTable = eval( ['table' num2str( order )] );    
 nPoints    = numel( indata );              % length of the time series
 opOrder1   = order + 1; 
 orderDelay = order*delay; 
 nPatterns  = factorial( opOrder1 );   % amount of ordinal patterns              
 patternsDistribution = zeros( 1, nPatterns ); % distribution of ordinal patterns
 outdata = zeros( 1, nPoints );        % values of permutation entropy    
 inversionNumbers = zeros( 1, order ); % inversion numbers of ordinal pattern (i1,i2,...,id)
 prevOP = zeros( 1, delay );           % previous ordinal patterns for 1:opDelay
 ordinalPatternsInWindow = zeros( 1, windowSize ); % ordinal patterns in the window
 ancNum = nPatterns./factorial( 2:opOrder1 );  % ancillary numbers       
 peTable( 1:windowSize ) = -( 1:windowSize ).*log( 1:windowSize  ); % table of precomputed values  
 peTable( 2:windowSize ) = ( peTable( 2:windowSize ) - peTable( 1:windowSize - 1 ) )./windowSize;       
 for iTau = 1:delay 
     cnt = iTau; 
     inversionNumbers( 1 ) = ( indata( orderDelay + iTau - delay ) >= indata( orderDelay + iTau ) );
     for j = 2:order
         inversionNumbers( j ) = sum( indata( ( order - j )*delay + iTau ) >= ...
           indata( ( opOrder1 - j )*delay + iTau:delay:orderDelay + iTau ) );
     end        
     ordinalPatternsInWindow( cnt ) = sum( inversionNumbers.*ancNum ); % first ordinal patterns
     patternsDistribution( ordinalPatternsInWindow( cnt )+ 1 ) = ...
       patternsDistribution( ordinalPatternsInWindow( cnt ) + 1 ) + 1;  
     for j = orderDelay + delay + iTau:delay:windowSize + orderDelay   % loop for the first window
         cnt = cnt + delay;                               
         posL = 1; % the position of the next point
         for i = j - orderDelay:delay:j - delay
             if( indata( i ) >= indata( j ) ) 
                 posL = posL + 1; 
             end
         end  
         ordinalPatternsInWindow( cnt ) = ...
           patternsTable( ordinalPatternsInWindow( cnt - delay )*opOrder1 + posL );
         patternsDistribution( ordinalPatternsInWindow( cnt ) + 1 ) = ...
           patternsDistribution( ordinalPatternsInWindow( cnt ) + 1 ) + 1;            
     end  
     prevOP( iTau ) = ordinalPatternsInWindow( cnt );
 end    
 ordDistNorm = patternsDistribution/windowSize;
 tempSum = 0;
 for iPattern = 1:nPatterns
   if ( ordDistNorm( iPattern ) ~= 0 )
     tempSum = tempSum - ordDistNorm( iPattern )*log( ordDistNorm( iPattern ) );
   end
 end
 outdata( windowSize + delay*order ) = tempSum;

 iTau = mod( windowSize, delay ) + 1;   % current shift 1:delay
 patternPosition = 1;                   % position of the current pattern in the window
 for t = windowSize + delay*order + 1:nPoints % loop over all points
     posL = 1;                          % the position of the next point
     for j = t-orderDelay:delay:t-delay
         if( indata( j ) >= indata( t ) ) 
             posL = posL + 1; 
         end
     end                          
     nNew = patternsTable( prevOP( iTau )*opOrder1 + posL ); % incoming ordinal pattern         
     nOut = ordinalPatternsInWindow( patternPosition );      % outcoming ordinal pattern 
     prevOP( iTau ) = nNew;
     ordinalPatternsInWindow( patternPosition ) = nNew; 
     nNew = nNew + 1;
     nOut = nOut + 1;       
     if ( nNew ~= nOut ) % update the distribution of ordinal patterns    
         patternsDistribution( nNew ) = patternsDistribution( nNew ) + 1; % incoming ordinal pattern
         patternsDistribution( nOut ) = patternsDistribution( nOut ) - 1; % outcoming ordinal pattern
         outdata( t ) = outdata( t - 1 ) + ( peTable( patternsDistribution( nNew ) ) - ...
                                        peTable( patternsDistribution( nOut ) + 1  ) );
     else
         outdata( t ) = outdata( t - 1 );
     end      
     iTau = iTau + 1; 
     patternPosition = patternPosition + 1;
     if ( iTau > delay ) 
       iTau = 1; 
     end
     if ( patternPosition > windowSize ) 
       patternPosition = 1; 
     end
 end 
 outdata = outdata( windowSize + delay*order:end )/log( factorial( order + 1 ) );