Jurnal Ship's Auxilary

7/25/2019 Jurnal Ship's Auxilary

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An expert system towards solving ship auxiliary machinery troubleshooting:

SHIPAMTSOLVER

Selcuk Cebi a,*, Metin Celik b, Cengiz Kahraman a, I. Deha Er c

a Department of Industrial Engineering, Istanbul Technical University, Macka 34367, Istanbul, Turkeyb Department of Maritime Transportation and Management Engineering, Istanbul Technical University, Tuzla 34940, Istanbul, Turkeyc Department of Marine Engineering, Istanbul Technical University, Tuzla 34940, Istanbul, Turkey

a r t i c l e i n f o

Keywords:

SHIPAMTSOLVERShip machinery troubleshootingShip auxiliary machineryMarine engineeringExpert systemDecision support system

a b s t r a c t

This study proposes an expert system to aid shipboard personnel for solving ship auxiliary machinerytroubleshootings. The developed expert system, called as SHIPAMTSOLVER, is structured based on PROLOGprogramming language. Principally, the SHIPAMTSOLVERsuggests an optimum action code for the ship-board personnel with respect to the established rules on technical knowledge, system indicators, and pre-vious failure experiences from the auxiliary machinery systems. As an illustrative case application, theSHIPAMTSOLVERis performed over a probable failure scenario in a ship refrigeration system. Utilizationof the SHIPAMTSOLVERon board merchant ships ensures an effective decision aid on corrective actionswhile reducing the response time to failures especially in manoeuvring positions and voyages in criticalwaters.

2008 Elsevier Ltd. All rights reserved.

1. Motivation

As common recognition of maritime statistical analyses (Han-sen, Nielsen, & Frydenberg, 2002; ONeil, 2003; Hetherington, Flin,& Mearns, 2006), the human error is highly cited contributing fac-tors for shipping accidents. Hence, the ongoing concerns about hu-man factor in shipping operations have forced the maritime societyto investigate for enduring solutions towards enhancing safety andenvironmental related precautions. Besides this motivation, themachinery failures, which are another casual factor of shippingaccidents in detail especially in port and terminals manoeuvrings(Antao & Guedes Soares, 2007), also require urgent strategies.The latent links between the system hardware and human errorrates are another significant viewpoint for the overall problem (Ce-lik & Er, 2007).

At this point, focusing on following actions can be addressed forthe desired solution in long-term perspective: (1) improving sys-tem design and installation, (2) increasing the level of supervisorysupport for shipboard personnel and (3) integration of recent mar-ine technology into shipboard operations. Among them, the firstitem is directly related to the shipbuilding process while the othersare taken advantage in operational process depending upon ensur-ing good communication in ship-shore interface and competencyaboard ships. In recent years, the pressures of international mari-time regulations enforce the shore-based management organiza-

tions of shipping firms to develop level of supervisory supportstowards merchant ships. However, the competency of seafarers isappeared as another critical barrier (Li & Wonham, 1999). There-fore, it is a great necessity for developing of personnel supportingtools based on information technology (IT) for shipboard opera-tions (i.e., repair and maintenance, troubleshooting, emergencyplanning, etc.). The additional software support especially towardsmaintenance process of ship machinery system (De Boer, Schutten,& Zijm, 1997; Deris, Omatu, Ohta, Shaharudin Kutar, & Abd Samat,1999; Mokashi, Wang, & Vermar, 2002) is able to reduce operatorsworkload, fatigue, and cognitive errors. Furthermore, the develop-ment of trouble diagnosis modules within system maintenancesoftware provide invaluable contributions for the shipboard per-sonnel to reduce the response time for failures especially inmanoeuvring positions and voyages in critical waters.

This paper mainly focuses on developing an advance tool basedon expert system principles towards solving ship auxiliary machin-ery troubleshooting (SHIPAMTSOLVER). In broad sense, expertsystems are computer applications, which embody some non-algo-rithmic expertise for solution of certain types of problems (Turban,1992). An expert system, also known as a knowledge-based sys-tem, is a computer program that contains the knowledge and ana-lytical skills of one or more human experts, related to a specificsubject. In this study, SHIPAMTSOLVERis developed based on PRO-LOG language. The PROLOG language has been shown to be usefulfor implementing expert systems (Walker, 1983). The primarilyreason why Turbo PROLOG 2.0 is used to develop SHIPMATSOLVERis to make the program possible to be used for personal computer

0957-4174/$ - see front matter 2008 Elsevier Ltd. All rights reserved.doi:10.1016/j.eswa.2008.09.060

* Corresponding author. Tel.: +90 212 293 1300x2746; fax: +90 212 240 7260.E-mail address: [email protected](S. Cebi).

Expert Systems with Applications 36 (2009) 72197227

Contents lists available at ScienceDirect

Expert Systems with Applications

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / e s w a
mailto:[email protected]://www.sciencedirect.com/science/journal/09574174http://www.elsevier.com/locate/eswahttp://www.elsevier.com/locate/eswahttp://www.sciencedirect.com/science/journal/09574174mailto:[email protected]


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(PC) which also has minimum configuration and prevent SHI-PAMTSOLVERfrom operating system failures of PC. This section en-ables the required motivation for this research. In Section2, thebackground and theoretical foundation of the SHIPAMTSOLVERareintroduced. The SHIPAMTSOLVER is driven over a probable failurescenario in a ship refrigeration system in Section 3. The last sectiongives the concluding remarks and further extension of the SHI-PAMT

SOLVERin practical applications aboard merchant ships.

2. Background of SHIPAMTSOLVER

2.1. Expert system practice in maritime literature

Expert systems are computer programs that are derived fromartificial intelligence (AI) research. AIs scientific goal is to under-stand intelligence by building computer programs that exhibitintelligent behaviour. The primarily aim of the expert systems isto aid an expert in making decisions about a certain problem.Knowledge-based systems can generally be defined as computersystems that store knowledge in the domain of problem solution.

Human experts rely on experience as well as on knowledge. Thisis the reason why problem-solving behaviours cannot be per-formed using simple algorithms. Experience can be regarded as aspecialized kind of knowledge created by a complex interactionof rules and decisions. The term expert system is typically usedfor programs in which the knowledge base contains the knowledgeused by human experts, which use additional information sourcesto solve a problem (Hemmer, 2008). Despite the availability ofexpert system applications in many scientific areas, the limitednumbers of studies (Kowalski, Arendt, Kapcia, & ski, 2001;Simoes-Marques & Pires, 2003; Flesche, Jalowy, & Inselmann,2004; Arendt, 2004; Lee, Lee, Park, & Kim, 2005; Kowalski,Meler-Kapcia, Zielinski, & Drewka, 2005; Lee et al., 2006; Cho,2006; Podsiadlo & Tarelko, 2006; Sharma, Kumar, & Kumar,

2008) can be cited regarding with the marine and shipboard sys-tems. The expert system developed for this study consolidatesthe experience- and knowledge-based on ship auxiliary machinerysystem troubleshooting.

2.2. Scope of SHIPAMTSOLVER: ship auxiliary machinery systems

The merchant ships are equipped with the auxiliary machinerysystems in order to serve the additional requirements for mainpropulsion system aboard ships. The consequence effects of trou-bles in shipboard auxiliary machinery directly or indirectly triggersthe failures in main propulsion system. The principal auxiliarymachinery systems on board merchant ships are as follows:(SAMS-1) FO-LO SEPARATOR SYSTEM, (SAMS-2) COMPRESSED

AIR SYSTEM, (SAMS-3) REFRIGERATION SYSTEM, (SAMS-4) FRESHWATER GENERATOR SYSTEM, (SAMS-5) STERN TUBE SYSTEM,(SAMS-6) STEERING GEAR, (SAMS-7) MOORINGS and WINCHES,(SAMS-8) HYDRAULIC CRANE, (SAMS-9) INCINERATOR, (SAMS-10) SEWAGE UNIT, (SAMS-11) BILGE SYSTEM, and (SAMS-12) OILYWATER SEPARATOR SYSTEM. The scope of SHIPAMTSOLVER is ar-ranged to cover the probable failures on these systems as well.

2.3. System components

Fig. 1 shows the basic structure of the SHIPAMTSOLVER, which in-cludes the user interface, a knowledge base, and an inferenceengine.

User interface provides an interaction among users, inference

engine, and knowledge base. It consists of inputs and outputs me-nus. The function of the user interface is to present questions and

information to the operator and to supply the operators re-sponses to the inference engine. Any values entered by the usermust be received and interpreted by the user interface. The infer-ence engine of the SHIPAMTSOLVER is the brain of the rule-basedsystem; it manages a large number of facts and rules, matchesrules against facts (data), and performs or delegates actions basedon the outcome. The role of the inference engine is to work withthe available information contained in the working memory andthe general knowledge contained in the knowledge base to derivenew information about the problem. This process is similar to theway a human reasons with available information to arrive at aconclusion. The inference engine, working either in the backward-or forward-chaining mode, will attempt to conclude new infor-mation about the problem from available information until some

goal is reached or the problem is solved. The inference engine ofthe SHIPAMTSOLVER is the backward-chaining because of proper-ties of the PROLOG language, which is used to develop SHI-PAMTSOLVER. The PROLOG inference engine does simplebackward-chaining. Each rule has a goal and a number of sub-goals. The PROLOG inference engine either proves or disproveseach goal of the developed program. Other portions of the system,such as the user interface, are also coded by using PROLOG as aprogramming language.

2.4. Development of the knowledge base

In this section, knowledge base is introduced by some codes andrules in SHIPAMTSOLVER. We cannot give the all codes that belong to

ship machine system for the shake of shortening the paper. A con-siderable amount of information and data in the knowledge base ofthe program was collected on the different types of ship machinerysystems, system failures, system indicators, and troubleshootingmethods through textbooks, handbooks, and interviews with chiefengineers who are the marine experts on ship machine systems.The system indicators such as gas temperature, LP pressure, HPpressure present a clue to marine experts to recognize the typeof system failure. Sometimes, indicators may point out thoseone-system failures more among many possible system failures.At this situation, while operating the system, expert can identifythe exact type of failure wholly. Otherwise, expert has to checkall possible failures. Then, failures are fixed by troubleshootingmethods, which are gained based on expert knowledge. The

knowledge base development was accomplished through the fol-lowing six steps:

Fig. 1. Basic structure of SHIPAMTSOLVER.

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Step 1. Identification of auxiliary machinery systems:The primar-ily goal of this step is to make operator focus on the system inwhich a failure occurs. It is previously defined 12 systems sinceSHIPAMTSOLVERtargets to solve the failures in shipboard auxiliarymachinery. These systems have been listed in Section2.2and theyare represented as SAMSiwherei defines auxiliary machinery sys-tem aboard merchant ships.

Step 2. Identification of probable system failure: In this step, theprobable failures are initiated using expert knowledge and previ-ous shipboard experiences on auxiliary machinery systems. Fail-ures are coded as Fj in knowledge base of SHIPAMTSOLVERwhere jis the number of relevant failure.

Step 3. Identification of causes for system failures: The potentialcauses for the probable system failures are identified and depictedas

Ckwhere

kdenotes the number of the cause.

Step 4. Identification of systemindicators:The measurable param-eters (i.e., temperature, pressure, power, level, etc.) related to oper-ating environment and system-working principles are primarilyidentified in order to integrate into SHIPAMTSOLVER. The systemindicator sets are defined as Imwherem is number of indicator.

Step 5. Identification of action codes: Action codes, which leadoperators in order to cope with probable system failures by consid-ering related causes, are defined. The defined action codes are pre-sented byAnwheren is number of the action code.

Step 6. Identification relations among the system indicators, causes

and action codes: The relation matrixes are constructed by inter-viewing maritime experts and utilizing system design knowledgeon shipboard auxiliary machinery. Then, the following relationsare established: (1) system indicators and causes and (2) causesand action codes.

Step7. Development of heuristicrules to resemble expertknowledge:

Integration of previously defined relations regarding with the sys-tem indicatorscauses and causesaction codes into proposed SHI-PAMTSPLVER is based on FUZZY IF THEN rules and IF THEN rules,respectively. For achieving this issue, trapezoidal fuzzy numbersareusedto depict thecharacteristics ofsystemindicators as inFig.2.

a b c d e

Low Normal High1

0Degreeof

Membership

System Indicators (Im)

Fig. 2. General representation of membership function for I m.

IF System is SAMSiAND Failure is Fj

ANDIndicators are ImTHENCause is Ck

IFCause is Ck THENActions are An

Fig. 3. Pseudo code for IF THEN rules.

USER INTERFACE

OPERATOR

SAMS1SAMS4

3

SAMS2

SAMS5

SAMSi..

..

.

.

.F1 F29 F35 . F41 F57 . F68 F7

F25 F26 F27.. Fj

I1, I2, I3, I4, I5. IN

Ck C147

C17

1

C35 C64

C12

C11

C1

C34

C25

.

..

A3An

A5

A21

A4

A33

A25

1

A17

A37

.

.

.

.

A3

C

A

SAMS

Fig. 4. Main framework of the knowledge base of SHIPAMTSOLVER.

S. Cebi et al. / Expert Systems with Applications 36 (2009) 72197227 7221


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In broad sense, the main structure of the defined rules can beexpressed as inFig. 3.

The relations and knowledge on a machine system are codedinto PROLOG language as rules and facts. PROLOG matches the goal of the SHIPAMTSOLVERwith the input data against the head of therules related goal and tries to match tail of the rule against facts

Fig. 5. Ship refrigeration system diagram.

Table 1

Instant data set for a ship refrigeration system

Indicators Code Unit Normal Instant values

Gas temperature I1 C 6080 85LP pressure I2 Bar 0.11.2 1.4HP pressure I3 Bar 1012 8Power I4 KW 6075 70Room temperature I5 C 18/21 16

Table 2

Causes for F1 in the refrigeration system

Causes

C1.LP pressure switch cut outsettings cuts

C2.HP pressure switch cut out settingscuts

C11. Insufficient refrigerant C21. Cut out limit was adjusted lower thannormal

C12. LP pressure switch failure C22. Mechanical failure in pressure switchC13. DP set point is low C23. Compressor capacity is too high

C14. Cut out set point is high C24. Insufficient cooling in condenserC15. Discharge valve on compressor is

leakyC25. Air, oil, and water in the system

C3.Expansion valve failure C4. Clogged evaporating coilsC31. Water in the system C41. Excessive refrigerant in the systemC32. Clogged filter within expansion

valveC42. Isolation problem in refrigerationrooms

C33. Bulb failure C43. Defrost timer is not working properlyC34. Wrong adjustment of expansion

valveC44. Feedback fault in thermostatic switch

C35. Gas leakage in solenoid valve C45. Broken fan blades

Table 3

System indicators for F1

Indicators Code Unit Low Normal High

Gas temperature I1 C 80LP pressure I2 Bar 1.2HP pressure I3 Bar 12Power I4 KW 75Room temperature I5 C (18)

Table 4

Action codes for F1

Action codes

A1. Check effectiveness of condensercooling

A13. Check oil level in the system

A2. Readjust HP pressure switch cut out A14. Check dryer filterA3. Readjust LP pressure switch cut out A15. Adjust expansion valveA4. Readjust LP pressure switch cut in A16. Clean expansion valve filterA5. Increase DPrange A17. Control expansion valve bulb

A6. Reduce compressor capacity A18. Control condition of evaporatingcoils

A7. Drain excessive refrigerant from thesystem

A19. Check or replace defrost timerdelay

A8. Refill the system A20. Adjust thermostatic valve limitsA9. Defrost evaporating coils A21. Check conditions of thermostatic

valve bulbA10. Replace expansion valve A22. Replace fan bladesA11. Repair solenoid valve A23. Remove air, oil, and water from the

systemA12. Check compressor suction &

discharge valvesA24. Replace LP pressure switch

A25. Replace HP pressure switch



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until finding all possible solutions.Fig. 4depicts main the frame-work of the knowledge base of the SHIPAMTSOLVER.

3. A case study: failure scenario on a ship refrigeration system

3.1. Description of a ship refrigeration system

Refrigeration on board merchant ship is based on evaporating aspecial liquid under controlled conditions. For this auxiliary sys-tem, the system components are needed for fine adjustment toset the system variables such as pressure, temperature, and power

in normal working conditions. The components and measurementpoints of the ship refrigeration system are illustrated inFig. 5. Thetypical ship refrigeration system includes the following main com-ponents: (1) air compressor, (2) filter, (3) dryer, (4) oil separator,(5) condenser, (6) expansion valve, (7) evaporating coils and (8)pressure switches. The performance of the system depends onintegrity of the relevant components.

3.2. Failure scenario

Based on the failure scenario over the system, the shipboardpersonnel in watch are aware of the irregularly starts and stopsof compressor.Table 1illustrates the instant data set for the sys-tem indicators during the occurrence of a failure. Without any sup-

Table 5

Relation matrix between system indicators and causes for F1

Causes I1 I2 I3 I4 I5

L N H L N H L N H L N H L N H

C11 * * * * *

C12 * * * *

C13 * * * * *

C14 * * * * *C15

* * * * *

C21 * * * * * *

C22 * * * * * * * * *

C23 * * * * *

C24 * * * * *

C25 * * *

C31 * * *

C22 * * * *

C33 * * * *

C34 * * * * *

C35 * * * * *

C41 * * * * *

C42 * * * * *

C43 * * * * *

C44 * * * * *

C45 * * * * *

Table 6

Relation matrix between causes and actions for F1

A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23 A24 A25

C11

C12C13C14C15C21C22C23C24C25C31C32C33C34C35C41C42C43C44

C45

55 60 70 80 85

Low Normal High1

0Degreeof

Membership

Gas Temperature (I1)

Fig. 6. Membership function for gas temperature.

-0.2 0.1 0.65 1.2 1.5

Low Normal High1

0Degreeof

Membership

LP Pressure (I2)

Fig. 7. Membership function for LP pressure.

9 10 11 12 13

Low Normal High1

0Degreeof

Membership

HP Pressure (I3)

Fig. 8. Membership function for HP pressure.



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porting instruments, shipboard personnel are faced with a greatnumber of causes for the targeted failure.

3.3. Failure scenario analysis SHIPAMTSOLVERbasis

Step 1: First of all, an operator inputs the system number inwhich the failure occurs. For the defined scenario, the SAMS-3 isentered.

Step 2: Some of these failures and their codes are given for arefrigeration system as follows; compressor starts and stops irreg-ularly (F1), compressor fails to start (F2), compressor starts butstops again immediately (F3), compressor operates continuously(F4), abnormal noise from compressor (F5), too little capacity oncompressor (F6), liquid stroke in compressor during start up (F7),liquid stroke in compressor during operation (F8), excessive con-denser pressure (F9), too low condenser pressure (F10), excessivesuction pressure (F11), too low suction pressure (F12), oil tempera-ture too low (F13), excessive discharge pipe temperature (F14), toolow discharge pipe temperature (F15), oil level in crankcase falling(F16), impossible to bleed plant (F17), crankcase sweating or frost-ing up (F18), refrigerant is leaking (F19) and suction pipes of com-pressor are iced (F20).

55 60 67.5 75 80

Low Normal High1

0Degreeof

Membership

Power (I4)

Fig. 9. Membership function for power.

-22 -21 -19.5 -18 -17

Low Normal High1

0Degreeof

Membership

Room Temperature (I2)

Fig. 10. Membership function for room temperature.

IF Compressor starts and stops irregularly

AND Gas Temperature NORMAL

ANDLP Pressure HIGH

AND Power HIGH

ANDRoom Temperature HIGH

THEN CAUSE:Discharge valve on compressor is leaky

ACTION: Check compressor suction & discharge valves


AND Gas Temperature HIGH

ANDLP Pressure HIGH

AND Power HIGH

ANDRoom Temperature LOW

THEN CAUSE: Compressor capacity is too high

ACTION:Reduce compressor capacity

Drain excessive refrigerant from the system

Fig. 11. Pseudo code for refrigeration system.


AND Gas Temperature 58

ANDLP Pressure 0.5

AND Power 77

ANDRoom Temperature -17.5

THEN CAUSE: Cut out limit was adjusted lower than normal OR

CAUSE:Mechanical failure in pressure switch

ACTION:Readjust HP pressure switch cut outANDReplace LP pressure

switch

Fig. 12. Pseudo code for crisp inputs.



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Step 3: Table 2 illustrates the causes for the failure thatcompressor starts and stops irregularly in the refrigeration system(F1).

Step 4:The system indicator sets are defined by marine expertgroups such as gas temperature, LP pressure, HP pressure, power,and room temperature. The indicator sets differentiate dependingon failure types.Table 3presents the system indicators and theirlinguistic values for F1.

Step 5: Actions that lead operators in order to fix refrigerationfailures are defined as inTable 4for F1.

Step 6:Tables 5 and 6illustrate the constructed relation matrixfor a refrigeration system.

Step 7:The relations between system indicators and causes arerepresented by IF THEN rules. In Figs. 610, trapezoidal fuzzy num-bers are used to depict the characteristics of system indicators.

Some of the rules, which define the relations among systemindicators, system failures, system causes and actions are as showninFig. 11.

In addition, it is possible to enter crisp numbers into SHI-PAMTSPLVER. When crisp numbers are entered to the system, the

C1

Failure Code: F1

C2

C11

C12

C13

C14

C15

C21

C22

C23

C24

C25

C3

C31

C32

C33

C34

C35

C4

C41

C42

C43

C44

C45

System Indicators

I1, I2, I3, I4, I5

Action Code: A1, A2, A3...An

USER INTERFACE

OPERATOR

Fig. 13. Main framework of the knowledge base of SHIPAMTSOLVER.

Fig. 14. Ship machinery system selection screen.



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Fig. 15. Failure mode selection screen.

Fig. 16. System indicators input screen.

Fig. 17. Cause and action code screen.



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system may give more than one causes for the failure (Fig. 12). Themain reason of this situation is that numbers between intersectionareas of linguistic terms.

The relations and knowledge on the machine system are codedinto PROLOG language as rules and facts. Fig. 13 depicts mainframework of the knowledge base of the SHIPAMTSOLVER.

3.4. Data input on SHIPAMTSOLVER. screen

As it has been mentioned before, one of the main objectivesestablished in the system design phase of this study is to burdenthe user with the minimum number of causes on system failuresas possible. This is accomplished using a backward-chaining infer-ence mechanism, which moves in step-by-step to identify thecauses and presents related actions. The first screen asks the oper-ators to select the system in which the failure occurred (Fig. 14).The expert system then presents the list of probable failures thatare needed to determine exact causes (Fig. 15). Then, the user isasked to enter the system indicator values (Fig. 16).

The input screens of the SHIPAMTSOLVERare easy to use andthere is not any item that conflicts users. All suitable causes forthe failure appear on green screen and the actions that must bedone by the crew are presented on third window of the SHI-PAMTSOLVER(Fig. 17).

3.5. Validation of the SHIPAMTSOLVER

The validation of the prototype expert system was accom-plished by an expert group consisting of three chief engineers ofship. Different types of case studies like mentioned above wereevaluated by the SHIPAMTSOLVER, and the results along with thereasoning process were compared against those obtained from hu-man expert group. Encouraging results were obtained from bothcase studies. Moreover, system can be validated in ship simulatorsystems or aboard merchant ships.

4. Conclusion

The complexity of marine systems and time constraints onoperational process require performing of effective corrective ac-tions towards managing a troubleshooting in auxiliary machineryaboard ships. SHIPAMTSOLVER, the proposed expert system in thispaper, ensures the required supervisory for shipboard personnel.It reduces the response time to failures and provides an invaluableadvantageous especially in manoeuvring positions and voyages incritical waters. Furthermore, the SHIPAMTSOLVER algorithm takesthe advantages of previous shipboard experiences and instant sys-tem data collaboratively. The practical application of SHI-PAMTSOLVER is illustrated with a troubleshooting in shiprefrigeration system onboard ship. As a further research proposal,the scope of the SHIPAMTSOLVERcan be extended to overall machin-ery system aboard merchant ships.

Acknowledgement

The authors are grateful to Chief Engineer Kemal Demirel fromMaritime Faculty of Istanbul Technical University for his valuablecontributions on this research.

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