Application of a novel immune network learn ing algorithm to fault diagnosis

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1 3 5 Vol CAA I Transactions on Intelligent System s Oct , 2, 1, 2 (1., ; 2., ) :,.,.,.,., 5. : ; ; ; : TP18 : A : (2008) Application of a novel immune network learn ing algorithm to fault diagnosis L I Hong2fang 1, 2, ZHANG Q ing2hua 1, X IE Ke2m ing 2 ( 1. College of Electronic Information and Computer, M aom ing University, Maom ing , China; 2. College of Information Engi2 neering, Taiyuan University of Technology, Taiyuan , China) Abstract: Immune algorithm s have p roblem s diagnosing faults in rotating machines. This is due to the volume of u2 nique data points they must p rocess, and the difficulty in elim inating redundant data. Hence, a novel imm une net2 work learning algorithm was formulated, in which antibody supp ression was introduced in the p rocess of generating initial antibodies, and a supp ression threshold for antibodies w ith respect to neighboring antibodies was defined. Redundant antibodies were elim inated, while allow ing the diversity of antibodies to be enhanced. new learning rate was defined, increasing the speed antibodies search in the direction of antigens. gorithm was tested in fault diagnosis for rotating m achines. fectively classify and recognize five typ ical kinds of faults. In addition, a Finally, the al2 Experimental results indicated that this algorithm can ef2 Keywords: clone selection; fault diagnosis; immune network; non2dimensional param eter,,. A IS, Timm is [ 4 ] De Castro [ 1 ] RLA IS ainet,. ( artificial immune. ainet system, A IS). [ 1 ].,,,,, [ 527 ]. [ 8 ] : : ( ) ; (2006B ). :. E2mail: lihongfang0109@163. com.,, [ 223 ]. A IS,,,. [ 9 ]

2 450 3,.,, : [ 4 ]. ainet,,,.. : = a gd, a (0, 1), gd = 2 m (m - 1) 6 m k = i+1 f A b i, A b j 1 (1) :, gd A b, m.,.. [ 0, 1 ],.,,.., [ 2 ]. 2,. : x + = x l p ( x) dx x m p ( x) dx 1 l 1 1 (2) m : x, p ( x). 2,,., f i, j,,.,. f i, j = D - 1 i, j. - 1 /2.,,. f i, j = D i, j, D i, j = A b i - A b j, i = 1,, N 1 (3), 1,, - 1 /2, D. D : ( 3), n A b ( n) ; :, f k, j, C % M; ,.,.,,,. n,, C,,.

3 5, : 451 N C = 6 n i = round (N - D i, j N ) 1 (4),,.. 2,, ;,. C k, C C = C k k N k (0, 2 ), = + k (A b i - C k ), 1 f i, j, k = 1,, N C 1 (5).., k N k (0, 2 ), = 1 f i, j, k = 1,, N C 1 0,. ( 5),,, f i, j,,, ; f i, j,,,,., : M D k, j > d ;,. : S i, k = M j, i - M j, k, Π i, k1 (6) S i, k < S ; S. M 3 j.,, ;,. 1. 4,. :,,, N INL 1 N INL Fig. 1 The frame of N INL algorithm

4 ainet 2 ( a) ( d), (1). 2 N INL Fig. 2 The performance analysis of the N INL algorithm :.,,.,.,.,,. 1 N INL a inet Table 1 The com par ing results of N INL w ith a inet ainet N INL ( a) 2 ( b) 2 ( c) 2 ( d) 2,, ( ) 5., 5,,, r/m in,, 1 024, 50, 50

5 5, : 453, 5. 2., 2 5 Table 2 The non2d im en siona l param eters of f ive faults : n 5; N 8; 20% ; 15; S d, (1). 2. 2, 5, N INL. 5.,,,,.. 4 N INL 5 Fig. 4 Classify the five kinds of faults with N INL 3,,,.,, ainet,., N INLA. 3 Fig. 3 The five faults of the non2dimensional parameter 3, 4. : [ 1 ]De CASTRO L N. An evolutionary immune system network for data clustering[ C ] / / Proceedings of B razilian Symposi2 um on Neural Networks. IEEE Computer Society Press, 2000:

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