Polymerizer fault diagnosis algorithm based on improved the GA-LMBP

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1 Avalable onlne Journal of Chemcal and Pharmaceutcal Research, 04, 6(6): Research Artcle ISSN : CODEN(USA) : JCPRC5 Polymerzer fault dagnoss algorthm based on mproved the GA-LMBP Shuzh Gao, Langlang Luan and XanWen Gao School of Informaton and Engneerng, Shenyang Unversty of Chemcal echnology, Shenyang, Chna School of Informaton and Engneerng, Shenyang Unversty of Chemcal echnology, Shenyang, Chna 3 School of Informaton Scence and Engneerng, Northeastern Unversty, Shenyang,Chna ABSRAC Amng at the PVC producton process s complex, the large crtcal devces polymerzer runnng need to constantly montor the characterstcs, performance montorng and fault dagnoss polymerzer for the large PVC batch producton process. Frst of all, for the lack of standard LMBP algorthm, the LMBP neural network algorthm s mproved; Secondly, based on the genetc algorthm (GA) and mproved LMBP algorthm that based on the GA-mproved a LMBP Polymerzer devce fault dagnoss algorthm s proposed; mproved LMBP algorthm and genetc algorthm (GA) combned algorthm s appled to the study of fault dagnoss polymerzer. Fnally, Combned wth polymerzer ndustral feld hstory data set to carry out fault dagnoss smulaton, results show the mentoned GA-mproved LMBP fault dagnoss method. s effectveness. Keywords: Polymerzer, Fault Dagnoss, LMBP neural network, Genetc algorthm (GA) INRODUCION PVC producton process s complex, producton safety and product qualty ndcators of requrements s hgh. he man raw materal for polyvnyl chlorde producton process s a vnyl chlorde monomer (VCM), generated by the polymerzaton kettle After the reacton of PVC products. he polymerzer s the key equpment of PVC producton unt, vnyl chlorde n polymerzaton occur the polymerzer reacton of PVC, the polymerzer stablty runnng drectly related to the health of PVC producton unt. Motor, speed reducer and machne closure s ensureded to the normal operaton of key equpment polymerzer devce. Once ther malfuncton wll be a serous loss to brng to producton. herefore, early dagnoss the polymerzer fault type and the reason, can avod polymerzer parkng caused huge economc losses, mprove the PVC product qualty, reduce producton costs has mportant practcal sgnfcance. In recent years, for the polymerzer fault dagnoss algorthm, many foregn scholars have proposed rough set neural network fault dagnoss method [-3], he rough set method as neural network lead system, smplfy the complexty of the neural network system, and mprove the precson and effcency of fault dagnoss. Many domestc scholars proposed reducton based on mproved dscernblty matrx roppertes polymerzer Rough Set - Neural network fault dagnoss [4-6], or the BP neural network expert system fault dagnoss [7-9], and prncpal component analyss n the polymerzaton producton process fault montorng and dagnoss [0-]. he tradtonal theory of BP algorthm of convergence s slow and that s easy to fall nto local mnmum value of the shortcomngs, t s dffcult to determne the number of hdden layers and hdden layer nodes. Improved LMBP 786

2 Shuzh Gao et al J. Chem. Pharm. Res., 04, 6(6): algorthm s appled to the polymerzer fault dagnoss, at the same tme usng a genetc algorthm (GA) to optmze the mproved LMBP neural network, select optmal weghts and thresholds. Because the genetc algorthm n every teraton of the evolutonary process s compettve gene retenton, whch means that the result of genetc algorthm s n search of the evaluaton functon of the sense of the optmal value. herefore, the paper proposes a combnaton based on the mproved LMBP neural network wth GA polymerzer ddagnostc algorthms. Frst, the rght value and the offset value of the LMBP algorthm ncremental G calculaton method wll be mproved, moved to the left-hand sde of the nverse matrx, Solvng equatons wth LU decomposton method; And then, wth a varable step sze nstead of a fxed step factor, whch s avod fallng nto the local mnmum value, so the calculaton s greatly reduced. LMBP neural networks of the proposed mprovements combned wth GA algorthms, combned wth the ndustral feld hstory data polymerzer fault dagnoss, and effectvely mprove the accuracy of fault dagnoss and dagnoss effcency. HE LMBP ALGORIHM OF IMPROVED SANDARD LMBP ALGORIHM Error objectve functon q n N ( ) = j = ( ) j= = = F x e v x () Where ej = tj yj () s the network error vector. v ( x) s the error vector. By Newtons method s ( ( )) ( ) x = k x k f x + k f xk (3) hen ( ) ( ) x = F x F x (4) Newton method has the advantages of rapd convergence, because each teraton of the calculaton can not be F x J x J x + S x approxmaton nstead guaranteed the Hessan matrx ( ) are reversble, Avalable ( ) ( ) ( ) F ( x), J ( x ), where e( x ) s the Jacoban (Jacoban) matrx. Hesson matrx N ( ) ( ) S( x) = e x e x = (5) ( ) ( ) e x e x L x x ( ) ( ) e x e x L J ( x) = x x M ( ) ( ) en x en x L x x n n n (6) It can be shown F( x) = J ( x) e( x) (7) When the soluton near the extreme ponts S( x ) = 0 (8) hen 787

3 Shuzh Gao et al J. Chem. Pharm. Res., 04, 6(6): ( ) ( ) ( ) ( x) = J x J x J ( x) e x (9) Formula to be mproved, so that t only contans the Gauss - Newton method and a mxed form of the gradent descent method. he formula for ( ) ( ) ( ) ( x) = J x J x + IU J ( x) e x (0) Where I s the unt matrx, U s a proportonalty factor, If U s close to zero, compared wth the Gauss - Newton method, If U value s large, smlar to the gradent descent method x F( x) / U, he usual adjustment strategy algorthm starts U that take a small postve value, If a step does not be reduced the error mesh value of the functon F( x ), U s multpled by a steppng factor greater than θ,that s U = Uθ, If a step produces a smaller F( x ), U n the next step dvded by θ,that s U = U / θ. HE LMBP ALGORIHM OF IMPROVED J J + IU LMBP algorthm to conduct n-depth research, nvolvng matrx s found to affect ts convergence, Matrx nverson, elmnatng tme-consumng by usng LU decomposton s greatly reduced LMBP amount of calculaton. Can be A = J J + IU () x = x () J e = b (3) hen (9) can be replaced by Ax = b (4) Can take advantage of symmetrc trangular decomposton LU drect decomposton of A.And Ax = b, the problem s equvalent to the calculated A = LU (5). Accordng to Ly = b (6) here Ux = y (7) Can be obtaned x. x used LU decomposton to solve wthout requre nverse matrx, Just n 3 / 3 tmes multplcaton and dvson, he operaton speed can be ncreased more than three tmes. Due to the reducton of the number operatons, not only the x n order to save computaton tme, but also to reduce roundng errors, herefore, ths mprovement makes a value calculated more accurately. Happens n the actual calculaton, wth the ncrease of U of small step, resultng n one teraton cycle requred n small steps n the cycle several tmes and spent a very long tme to end. In order to solve ths problem, the orgnal fxed value of θ s desgned to be varable step sze,.e. the step length factor θ s a varable amount. Varable step sze formula s defned as θ k = α θ (8) Where k s the only enter ths step the number of small cycles, α [ 0,] for the adjustment varable. If a step does not reduce the value of the error mesh functon F( x ) s multpled by a new steppng factor produces a smaller F( x ), then n the next steppng factor θ dvded by the new. hat U U θ θ, U = /. = Uθ. If a step 788

4 Shuzh Gao et al J. Chem. Pharm. Res., 04, 6(6): GA-IMPROVED LMBP ALGORIHM GA-IMPROVED LMBP OPIMIZAION ALGORIHM SEPS he genetc algorthm (GA) s a smulaton of Darwns genetc selecton and the natural selecton process of bologcal evoluton calculaton model, t s frst proposed n 975 by the Unted States executve root (Mchgan) Unversty J.Holland professor, has a strong genetc algorthms macro-search capablty and global optmzaton performance[3]. LMBP neural network structure, the choce of ntal connecton weghts and thresholds of the network tranng performance s good or bad for a great mpact, but can not be accurately obtaned, for ths feature, ths paper uses a genetc algorthm neural network weghts and valve values to be optmzed. A combnaton of genetc algorthm and mproved LMBP algorthm and tranng weghts and thresholds of the neural network usng genetc algorthm to fnd narrow your search usng mproved LMBP network to the exact soluton can reach a global fnd and fast effcency purposes. Has a three-layer BP network, I s the output of the j-th node of the nput layer, H s the output of the -th node n the hdden layer. O s the output of the -th node n the output layer; WIH s the -th node and the j th hdden layer node n the nput layer connecton weghts; connecton weghts of the -th node. j WHO s the j-th node n the hdden layer and output layer j he genetc algorthm learnng mproved BP network, follow these steps: ( ) Intal populaton p, ncludng cross-scale crossover probablty pc and mutaton probablty pm, As well as any one of WIH and WHO ntalzaton; In the encodng, usng the real number codng, the ntal populaton value of 30. j j ( ) Calculated for each ndvdual evaluaton functon, and sort. Press-probablty values. P = f / s f = (9) N Select network of ndvduals, where squared errors E. f = / E ( ) (0) f s the ndvdual wth the real value, Avalable to measure the sum of E( ) = ( Vk k ) p k () Where =, L, N represents chromosome; k =, L,4 s the output layer nodes; s s the number of learnng samples; for teachers sgnal. k ( ) he probablty pc the ndvdual G and G + crossover operator to produce new ndvduals G + of no cross ndvduals drectly coped. ( ) Usng probablty pm mutaton produce G j new ndvduals 789 G j of. G and ( ) New ndvduals nto the populaton p, and calculate the new evaluaton functon of the ndvdual. ( ) If you fnd a satsfactory ndvdual ends, otherwse go to (III). Acheve the requred performance; the decoder can be obtaned wth the best ndvdual n the fnal populaton optmzed network connecton weghts. GA- IMPROVED SIMULAION OF LMBP ALGORIHM he fault dagnoss system of ths artcle s based to polymerzer a large chemcal group 70 M3, for example, the analyss of the measured data polymerzer fault montorng system. he polymerzer nclude motor reducer Mechancal seal and mechancal seal. Decson table, select S, S, S3, S4, S5, S6, S7, S8, condtonal attrbutes correspondng sgns, Namely: water flow seal polymerzaton temperature ( C), polymerzaton reacton pressure (MPa), and strred for current (A), Water flow seal((m3/h),the jacket flow (m3/h), the coolng water temperature ( C), coolng water, water pressure (Mpa)outlet shutter temperature ( C), the correspondng varables for the a, b, c, d, e, f, g, h. ypcal LMBP neural network for the polymerzer fault dagnoss system n ths artcle, the nput layer nodes 6, the number of nodes of the output layer 4, and 0 experments after

5 Shuzh Gao et al J. Chem. Pharm. Res., 04, 6(6): select hdden layer nodes. he polymerzer fault dagnoss LMBP neural network structure for N (6,0,4). able Hstorcal data of Polymerzer Sample(U) Hstorcal datum of polymerzer a, S b, S c, S3 d, S4 e, S5 f, S6 g, S7 h, S M M M M M M M M M able rang sample data se Sample(U) Hstorcal datum of polymerzer a, S b, S c, S3 d, S4 e, S5 f, S6 g, S7 h, S M M M M M M M M M able 3 estng sample data set Sample(U) Hstorcal datum of polymerzer a, S b, S c, S3 d, S4 e, S5 f, S6 g, S7 h, S M M M M M M M M M Improved LMBP In order to compare the performance of the algorthm, smulaton LMBP algorthm for standard and mproved LMBP algorthm, GA-LMBP algorthm and the GA-mproved LMBP comparson. () he LMBP algorthm, mproved wth the preparaton of ther own Mytranlm subroutne tranng tmes net.epochs = 000, the tranng goals net.goal = 0.000, learnng rate LP.lr = 0.. Fgure Improved LMBP algorthm convergence tmes 790

6 Shuzh Gao et al J. Chem. Pharm. Res., 04, 6(6): () GA-mproved the the LMBP algorthm for (populaton sze NIND = 40, heredtary algebra MAXGEIN = 50 ndvdual length PRECI = 0, crossover probablty pc = 0.7, mutaton probablty pm = 0.0). Fgure he LMBP algorthm n GA-mprovement convergence Vews Can be summed up by the followng able 4: the four mproved LMBP algorthm n standard LMBP algorthm the number of teratons s, the error s , s most unsatsfactory. Iteraton of the GA-mproved LMBP algorthm tmes whle the error s only deal result, tranng n the fastest, most accurate polymerzer fault dagnoss. able 4 Calculaton tmes and ranng error of four knds of amelorated BP algorthm ImprovedBP Algorthm LMBP algorthm Improved LMBP algorthm GA-LMBP algorthm GA-mprovedLMBP algorthm he number of teratons 6 7 ranng error RESEARCH ON FAUL DIAGNOSIS ALGORIHM OF POLYMERIZAION KELE Research on fault dagnoss Algorthm for Large polymerzaton kettle, fault code type s defned. he correspondng polymerzer fve state based on LMBP Neural Network output codes are: normal runnng (0000), Motor (000), Mechancal seal ( 000 ), gear (000), machne component (000). By GA-LMBP neural network fault dagnoss tests on polymerzaton kettle, frst select the table that corresponds to 3 of the 0 sets of data, or 80 per cent sampled-data as a test sample, the smulaton results as shown n fgure 4, tran number of steps s, the error s , the target error of 0-4.Fault dagnoss n table 5, ths tme fault dagnoss accuracy rate of 00. able 5 troubleshootng results est sample BP output Fault Code Dagnoss type Machne component Motor Mechancal seal 0 groups of data from hstorcal databases and select troubleshootng, dagnostc results as shown n table 6, s7 the 7th set of data should be the normal operaton of the data, dagnostc results show 000, show that gear, the results of the 7th set of data s n error. herefore, the dagnostc accuracy of

7 Shuzh Gao et al J. Chem. Pharm. Res., 04, 6(6): able 6 troubleshootng results est sample BP output Fault Code Dagnoss type Motor Mechancal seal Machne component 79 Machne component Another 0 sets of data mport from the database troubleshootng, dagnostc results as shown n table 7, ths tme fault dagnoss accuracy rate s 00. able 7 troubleshootng results est sample BP output Fault Code Dagnoss type Machne component Norma Mechancal seal You can see from three dagnostc results, wth ga- lmbp algorthm for fault dagnoss of Polymerzaton kettle s very effectve, the accuracy rate of Fault Dagnoss by mutatons n the varable data, you can determne the aggregaton of faults n reactor equpment, such as mxng current for the of motor faults and gear, water flow of the shaft seal s the of mechancal seal, Polymerzaton temperature s too hgh or hgh pressure wll cause damage to the machne components drectly. CONCLUSION hs paper presents a fault dagnoss algorthm based on GA-mproved a LMBP Polymerzer devce of the weghts and the bas value ncrement LMBP algorthm nto the change, frst of all, the nverse matrx moved the equaton on the left, wth LU drect decomposton method for solvng equaton, so that the computatonal complexty for reducng. hen, wth a varable step sze nstead of the fxed steppng factor, reducng the number of cycles. he mproved LMBP algorthm and genetc algorthm (GA) combned fault dagnoss algorthm appled to the polymerzaton kettle fault dagnoss research, smulaton results show that ths algorthm n fault dagnoss polymerzer tranng speed, hgh precson and practcalty, faled the accuracy rate of Acknowledgments hs work was supported by the Key Program of Natonal Natural Scence Foundaton of Chna ( ), the Pos tgraduate Scentfc Research and Innovaton Projects of Basc Scentfc Research Operatng Expenses of Mnstry of Educaton (N ). REFERENCES [] X. D. Yu,H. Z. Zang,"ransformer fault dagnoss based on rough sets theory and artfcal neural networks//proc.of008 Internatonal Conference on Condt Montorng and Dagnoss," New York:IEEE. 008, [] X. F. Feng,Z. X. Nu,etal,"A parttonng decson way for fault dagnoss//proc of 4th Internatonal Conference on Natural Computaton," NeYork:IEEE, 008, [3] Y. G. Wang,B. Lu,Z. B. Guo, et al,"applcaton of rough set neural network n fault dagnosng of test-launchng control system of mssles//proc 5th World Congress on Intellgent Control and Automaton," New York:IEEE, 004, [4] H. X. L,X. Z. Zhou, Journal of Central South Unversty ( Journal of Central South Unversty ), 009,40(supp,), [5] D. Q. Yan, K. Q. L,Z. X. Ch, Journal on Communcatons(Journal of communcaton),009,30(8), [6] Y. Yao,Y. Zhao, Informaton Scences,009,7(79),

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