Fuzzy Classification and Implementation Methods for Tugboat Main Engine Fault

Size: px

Start display at page:

Download "Fuzzy Classification and Implementation Methods for Tugboat Main Engine Fault"

Lorena Carter
5 years ago
Views:

1 I MATEC Web of Conferences 95, (2017) DOI: / matecconf/ Fuzzy Classfcaton and Implementaton Methods for Tugboat Man Engne Fault Xaohu Xu, Guangchen Ba School of Automaton, Bejng Unversty of Posts and Telecommuncatons, Bejng, Chna Abstract. The establshment of classfcaton ndex system of fault n tugboat man engne was brefly dscussed frst. After that, the fuzzy method and procedures used n fault classfcaton were ntroduced. Several mplementaton technques of the fuzzy classfcaton were explored ncludng the knowledge expresson, the herarchcal structure of knowledge lbrary, the pre-processor and the forward nference engne. Some specfc fault records of tugboat man engne were analyzed and classfed for certfcaton. And results prove that fuzzy classfcaton has certan reference value and mproves the workng effcency of tugboat personnel n equpment mantenance and management. 1 Introducton As an mportant auxlary shp for towng larger vessels n harbor, tugboat has the characterstcs of flexble operaton and beng hghly maneuverable. However, at the same tme t makes the load of tugboat man engne change dramatcally, where the workng condton s extremely severe. When t comes to tugboat fault dagnoss, personnel often relate to unquantfable concepts such as relatvely low pressure, nsuffcent flow and excessve temperature rse, whch have not defnte boundares, namely fuzzy concept. In addton, a fault may result n a varety of phenomena, especally for the heavy complcated engneerng equpment ntegrated wth electromechancal and hydraulc system. Determnstc factors and random factors beng staggered together, tugboat man engne fault has the dstngushng feature of changng gradually and concealment[1]. Therefore, t s necessary to explore the classfcaton ndex system of tugboat fault. By researchng the classfcaton method and ts mplement technques, tugboat fault can be analyzed and classfed, layng the foundaton for the subsequent research. Tugboat fault classfcaton wll help personnel further dentfy the exact locaton and cause of fault, predct the trend of fault and potental danger, and thus determne the approprate measures to be taken. 2 Fault classfcaton ndex system Tugboat works n the harbor, whch s hghly maneuverable and has hgh work ntensty,therefore tugboat man engne adopt four-stroke medum speed desel engne, wth the advantages of compact structure and hgh power. Tugboat man engne workload s varyng wdely, frequent starts and stops render the mechancal load and thermal load change remarkably, leadng to the extremely bad workng envronment, even a fault or stallng the engne. Accordngly we are supposed to make prelmnary judgment on the fault phenomena tmely, n an attempt to gan tme for dagnoss and mantenancem. Tugboat man engne fault system are as follows: ventlaton system, fuel system, lubrcatng ol system, seawater system, freshwater system, startng ar system, pressurzaton system. Then the fault phenomena havng been analyzed and classfed, a classfcaton ndex system of tugboat man engne fault s ntally bult. The system classfy faults wth some certan ndex as shown n Fgure 1. Abnormal Gas Exhaust Fault Contents Engne Work Excepton Fault Classfcaton Index Tree Instrument Abnormal Index Fault Numbers Fgure 1. Fault Classfcaton Index Tree. One Excepton Occurs Fault Structure 3 Fuzzy methods and procedures of fault classfcaton [2] Fault n tugboat man engne s a relatve and qualtatve fuzzy concept. It s dffcult to have a quanttatvely accurate represent of classfcaton ndex as the contents are rch and formats are varable, whch renders the Twe Exceptons Occur All Exceptons Occur The Authors, publshed by EDP Scences. Ths s an open access artcle dstrbuted under the terms of the Creatve Commons Attrbuton Lcense 4.0 (

2 classfcaton a mult-factor and mult-level fuzzy classfcaton. A herarchcal factor-based fuzzy classfcaton method s used n the classfcaton, ths s also to say, to classfy from the bottom to the top, as well as from a sngle ndcator to mult-ndcator consdered. 1).The classfcaton ndex doman of the classfcaton object s determned, then dvded nto n subjects. n Uu 1 [3] p where 1 2 U (1) u u, u,, u ( ( 1,2,, n) 2). To each classfcaton ndex subsets u, perform the sngle level and sngle ndcator fuzzy transformaton. Classfcaton level doman V v1, v2,, vq s gven, where q s better to be an odd number so that we can have a mddle level. Quantfy each ndcator n u, the membershp of a certan fault to all categores classfed by sngle ndcator s determned. The value of membershp functon can be determned usng expert scorng method. So we can have the sngle ndcator fuzzy matrx R of u. The fuzzy weght vector of every ndcator n u s determned as A ( a, a,, a ) 1 2 p Before performng the synthess process, A needs to be normalzed. Ensure p ar 1 (2) r1 Performng fuzzy transformaton to A and R gves us B, the sngle level classfcaton result vector of, whch s [3] A R B ( 1,2,, s ) (3) B ( b 1, b2,, b ) m The fuzzy weght vector of comprehensve ndex u ( 1,2,, s ) s gven as follow [3] A ( a1, a2,, a s) 3). Mult-level fuzzy classfcaton. Repeat steps 1~2 usng u as the comprehensve ndex and B as the sngle ndcator classfcaton result. We can have Mult-level fuzzy classfcaton model. 4). Analyss of fuzzy classfcaton results. The weghted average method s used to process fuzzy classfcaton result vectors. The basc dea of ths process s frstly to get the rank of classfcaton result vector accordng to ts poston, whch s to use 1, 2,,q q to represent every categores, and then secondly to get the weghted sum of each components rank of result vector, thus we can have the relatve poston of classfed object, wrtten as q q k k j j j1 j1 E b j b where s a coeffcent to be determned for controllng the mport of larger. s often set as 1 or 2, so we can sort several classfed objects n the order of E [3]. 4 Key technques n classfcaton 4.1 Knowledge expresson Producton rule Abnormal gas exhaust, engne work excepton as well as nstrumental abnormal ndex and the fault structure are dentfed and summarzed as knowledge, based on the aforementoned classfcaton ndex system. The knowledge s expressed as producton rule wth a basc structure below: IF A THEN B In whch, A s the producton prerequste, and B s a set of conclusons. Both of A and B can generously be expressons composed by text, numbers and logcal operators AND, OR, NOT. Here n our research, the prerequste A led by IF of producton rule s the classfcaton ndex of dfferent levels, and concluson B led by THEN s the correspondng membershp functon Frame structure Correspondng to the herarchcal structure of classfcaton ndex tree, the multple nested frame structure, such as {Frame{Sub-Frame{Slot{Sde{Sub- Sde{Value }}}}}}, s used for the knowledge expresson to manage the producton rules. The framework s composed by dfferent levels. Its basc logcal structure can be expressed as: {Frame{Slot 1{Sde 1{Value 1, Value 2, Value m} Sde 2{Value } Slot 2{ } Slot n{ } }} In ths structure, locaton frame s for the classfed object, such as a certan fault record. Slot s for the classfcaton ndex, such as the fault content. Sde s for classfcaton sub-ndex, such as gas exhaust. Value s for classfcaton ndcator, such as exhaust ppe s blowng out black smoke. Compatble wth the ndex, one sde can have lots of value where producton rules would be stored. Ths frame-rule mxed expresson of knowledge can greatly smplfy the workload of the nference engne, (4) 2

3 reduce the search volume compared wth a smple rule system, shorten the reasonng tme and mprove the effcency of solvng. 4.2 Herarchcal structure of knowledge base A comprehensve knowledge base s necessary for the classfcaton. Herarchcal tree structure, as shown n the Fgure 2, s used to buld to knowledge base, consderng the contents and structure of classfcaton ndex system[4]. Body fault Tugboat Man Engne Common Faults Ventlaton system fault Fuel system fault Outsde the body fault Exhaust Condton Lubrcatng ol system fault Seawater system fault Engne Condton Freshwater system fault Startng ar system fault Instrument Index Body remote fault Pressurzaton system fault (a) Top level of knowledge base structure Fuel System Fault Injecton pump fault Fuel njecton fault Injecton tmng fault whch the knowledge wll also be convenently used n nference. 4.3 Pre-process and nference Pre-processor Demanded by the classfcaton knowledge, feature factors n a fault phenomenon must be extracted to compose the fact lbrary. The pre-processor we used on our research s descrbed as follows. For a complete fault record, pre-processor frstly fltered the contents of features n t, such as many knds of exhaust gas condton, engne condton, nstrumental ndex, to compose the Feature Factor Sets. Then the number of all knds of factors, whch s structurally correspondng wth the orgnal text, s extracted after the flterng and countng. The results consst the fact lbrary, and wll be matched wth the perquste of rules. A record s gven here. A fault record n tugboat man engne we collected s Exhaust ppe blows out black smoke and ts temperature ncreases; unstable work wth sudden exploson then power and speed decrease; at the same tme, engne fuel consumpton rses wth ol tube of hgh temperature severe vbraton; coolng water`s temperature ncreases; temperature of lubrcatng ol rses and ts pressure drops. The nformaton extract after pre-processng s lsted n Table 1. Table 1. The pre-processor result of a fault record. Engne fuel consumpton ncreases Wear of fuel pump Plunger and sleeve jammng Wear of needle valve Nozzle fault Needle valve jammng Small njecton advance angle Largel njecton advance angle (b) Second level of knowledge base structure Fuel Injecton Fault Wear of needle valve Nozzle fault Needle valve jammng Exhaust ppe pp blows out black smoke Exhaust temperature rse Engne startup fault Lubrcatng ol temperature rse Lubrcatng ol pressure drop Coolng water temperature rse Ol tube severe vbraton Speed s reduced or even turn off (c) Bottom level of knowledge base structure Fgure 2. Herarchcal Structure of the Knowledge Base. The storage, management and mantan of knowledge can be easly acheved wthn ths structure[5], through Unstable work wth sudden exploson Content Fault Name Number Exhaust gas condton Engne condton Instrumental ndex Inference engne Blowng out black smoke Exhaust gas`s temperature rses Engne power decreases Unstable work wth sudden exploson Ol tube`s severe vbraton Engne fuel consumpton rses Coolng water`s temperature rses Lubrcatng ol`s temperature rses Speed decreases Lubrcatng ol`s pressure drops Ftted wth the knowledge expresson, forward nference engne s appled n our research. By matchng the fact lbrary wth the prerequste of rules, the values of membershp functon can be determned through the nference engne. 3

4 Indcators n dfferent levels of ndex system are flled n the herarchcal structure of knowledge base. The slot and sde that wll be used accordng to the levels of classfcaton are frstly determned by the nference engne, and then the rules needed n solvng are searched. Contents n fact lbrary s matched wth the prerequste of the rules n the rule lbrary one by one. The membershp functon contaned n the concluson of rules successfully matched wll be stored as one of the row elements of membershp matrx. 5 Implementaton The collected fault record n tugboat man engne are classfed wth the methods above. To space lmtatons, the fault record n 4.3 s gven next as an example of the general classfcaton. Gven the classfcaton categores doman V. V={Ventlaton system fault (V), fuel system fault (F), lubrcatng ol system (L), seawater system fault (S), freshwater system fault (W), startng ar system fault (A), pressurzaton system fault(p)} Man ndcators are chosen, accordng to the contents referred, to buld classfcaton ndcators doman U The general ndcators doman used n classfcaton of dfferent fault record s transcendental, whle dfferent knds of sub-sets of ndcators can also be generally used n classfcaton of the same type of faults. The classfcaton result s shown n Table 2. In categores. represents Blowng out black smoke whle s on the behalf of Exhasut gas`s temperature rses and the rest are nferred n Table 1. Categore s Table 2. The classfcaton result of a fault record. V F L S W A P However, when data n Table 2 are used as the fuzzy matrx R, the value of each component of weght vector s too small to affect the classfcaton results snce there are too many categores. So we dvde the data nto 3 groups(the frst two as a group, and as a group, the last four as the thrd group). Fuzzy transformaton s performed respectvely to have Delph method s used to determne the weghts of each ndcators. Here, we use the weght vector A=0.21, 0.43, 0.36 Fuzzy transformaton s performed to have B=0.21, 0.43, 0.18, 0.07, 0.13, 0.02, 0.13 Weghted average method s used to analyss and process the fuzzy classfcaton results. Set the value of each components of the resultng vector accordng to ther postons,whch means that, 1 for ventlaton sysytem fault, 2 for fuel system fault, 3 for lubrcatng ol sysytem fault, 4 for seawater system fault, 5 for freshwater system fault, 6 for startng ar system fault, 7 for pressurzaton system fault. The fnal classfcaton results s calculated as Equaton (4)( =2, q =7). The calculaton result s 2.45,whch shows that t s between category 2 and 3, and slghtly partal to the second one. Thus t s probably a fuel system fault[6] or a lubrcatng ol system fault, though more lkely to be the former. However, t can easly dentfed lterally to both of those two categores. As for whch one t actually belonged to, these two systems should be checked to determne the factual fault locaton. Nevertheless, n terms of the phenomenon of the fault record, t s dffcult to dstngush whether t s a fuel system fault or a lubrcatng ol system fault. Therefore, ths classfcaton result s reasonable. 6 Implementaton Wth method and procedure above, a fault record n tugboat s classfed for certfcaton, and the result s consdered reasonable and useful whle the method and mplementaton are effectve and effcent. Result shows that the classfcaton ndex system, the method and the mplementaton technque can not only satsfy the actual need of the tugboat man engne fault classfcaton, and also enable personnel to elmnate the fault as soon as possble, to gan tme for the normal operaton of tugboat. References 1. Tkun Wang: Tugboat man engne fault dagnoss method based on the fault tree analyss (Master of Engneerng, Dalan Martme Unversty, Chna 2012) 2. Xaohu Xu, Bngshu Tong. Fuzzy evaluaton and mplementaton technques of product structure desgn for assembly. Agrcultural Machnery. 38,2 (2007), p

5 3. Zhongxong He :Fuzzy mathematcs and applcatons(tanjn scence and technology press,chna,1982) 4. Qngmn Zhong: Expermental research on montorng and dagnosng for desel engne(master of Scence, Wuhan Unversty of Technology, Chna 2008) 5. Yanyou Cha: Research on fault dagnoss of marne desel engne based on kernel-based learnng theory(doctor of Engneerng, Harbn Engneerng Unversty, Chna 2012) 6. Xaojun Ma, Zh Wang, Chunguang Xng. Research on fault dagnoss for fuel njector system of marne desel engne. Shp scence and technology. 30,6 (2008), p ,103 5

BioTechnology. An Indian Journal FULL PAPER. Trade Science Inc.

BioTechnology. An Indian Journal FULL PAPER. Trade Science Inc. [Type text] [Type text] [Type text] ISSN : 0974-74 Volume 0 Issue BoTechnology 04 An Indan Journal FULL PAPER BTAIJ 0() 04 [684-689] Revew on Chna s sports ndustry fnancng market based on market -orented