Automotive troubleshooting process integrates repairing activities that are executed through auto professionals when they note phenomenon or conditions and determine about inspections, instructions, or checks, so as to tackle the trouble that affects a car. This study is focused on the knowledge representation for the aim of decision making in automotive troubleshooting process for automotive braking system. To reach this purpose, there are three phases followed: (1) a knowledge representation with procedural mode is investigated from an aspect of decision making; (2) a simple, instinctive, and efficient architecture of automotive knowledge formalization is presented; (3) an approach to generate troubleshooting procedures is defined. A new form, named diagram of expanded transformation (DoET), to represent knowledge and depict three fundamental tiers of decision making in the present or future disposal: processing strategy, quantity, and inapplicability. The approach can be also utilized manually to create DoETs from auto repair manuals (ARMs) or to build them spontaneously applying the messages feasible on workshop lists regarding single, multi-tier troubleshooting processes. The DoETs with auto repair manuals for auto braking system is validated. The acquired model can be utilized as a base structure for troubleshooting assisted systems generation.
LiangJS.Generation of automotive troubleshooting configuration system using an ontology-based approach. Comput Ind2012; 63(5): 405–422.
3.
LiangJS.The service task implementation in automotive troubleshooting using an ontology-based knowledge support system. Proc IMechE, Part D: J Automobile Engineering2014; 228(13): 1599–1621.
4.
LiangJS.A process-based automotive troubleshooting service and knowledge management system in collaborative environment. Robot Comput Integr Manuf2020; 61: 1–17.
5.
ReddyEJSridharCNVRangaduVP.Knowledge based engineering: notion, approaches and future trends. Am J Intell Syst2015; 5(1): 1–17.
6.
StenholmDMathiesenHBergsjoD.Knowledge based development in automotive industry guided by lean enablers for system engineering. Procedia Comput Sci2015; 44: 244–253.
7.
AdekunleALkubanniPPAgboolaOO.An expert system for automobile repairs and maintenance. Mindanao J Sci Technol2018; 16: 41–56.
8.
MilanaMKhanMKMunive-HernandezJE.Design and development of knowledge based system for integrated maintenance strategy and operations. Concurr Eng2017; 25(1): 5–18.
ReijersHAMansRSvan der ToornRA.Improved model management with aggregated business process models. Data Knowl Eng2009; 68(2): 221–243.
14.
KruseRSteinbrecherMMoewesC. Data mining applications in the automotive industry. In: Fourth international workshop on reliable engineering computing (REC 2010), Singapore, 3–5 March 2010, pp.23–40.
15.
DhanalakshmiDLakshmiJK.A survey on data mining research trends. Int J Eng Comput Sci2014; 3(10): 8911–8919.
16.
StecułaKChikhradzeM.Use of the decision table for supporting management process in mining companies. Multidiscip Asp Prod Eng2018; 1(1): 587–593.
17.
van GlabbeekR. On the meaning of transition system specifications. In: Combined workshop on expressiveness in concurrency and structural operational semantics, Amsterdam, 26 August 2019, pp.69–85.
18.
LierlerYTruszczynskiM. Transition systems for model generators: a unifying approach. In: 27th international conference on logic programming, Lexington, KY, USA, 6–10 July 2011, pp.629–646.
EleBIEleSIOfemAO.Development of an intelligent car engine fault troubleshooting system. West Afr J Ind Acad Res2016; 16(1): 38–50.
21.
AngeliC.Diagnostic expert systems: from expert’s knowledge to real-time systems. In: SajjaPAkerkarR (eds) Advanced knowledge based systems, model, application & research, Chennai, Tamilnadu: TMRF e-Book, 2010, pp.50–73.
22.
BudihartoW.The development of an expert car failure diagnosis system with Bayesian approach. J Comput Sci2013; 9(10): 1383–1388.
23.
BernaertMPoelsG. The quest for know-how, know-why, know-what and know-who: using KAOS for enterprise modelling. In: International conference on advanced information systems engineering, London, 20–24 June 2011, pp.29–40.
24.
de Ginja AntunesHPinheiroPG. Linking knowledge management, organizational learning and memory. J Innov Knowl2019; 5(2): 140–149.
25.
RenziCLealiFDi AngeloL.A review on decision-making methods in engineering design for the automotive industry. J Eng Des2017; 28(2): 118–143.
26.
RuizRMarotoCAlcarazJ.A decision support system for a real vehicle routing problem. Eur J Oper Res2004; 153(3): 593–606.
27.
AdamczykBSSzejkaALJuniorOC, et al. Towards semantic interoperability approach to support the calibration process of electronic engine management system. In: Proceedings of the 25th ISPE Inc. interantional conference on transdisciplinary engineering, Amsterdam, 3–6 July 2018, pp.1024–1043.
28.
van LangeveldeIPhilipsenATreurJ. A compositional architecture for simple design formally specified in DESIRE. In: TreurJWetterT (eds) Formal specification of complex reasoning systems. New York, NY: Ellis Horwood, 1993, pp. 277–294.
29.
FenselD.The knowledge acquisition and representation language KARL. Boston: Kluwer Academic Publ, 1995.
30.
van HarmelenFBaldeJ. (ML)2: a formal language for KADS conceptual models. Knowl Acquis1992; 4: 127–161.
31.
SchreiberGWielingaBAkkemansH, et al. CML: the CommonKADS conceptual modeling language. In: SteelsLSchreiberGVandeVeldeW (eds) A future of knowledge acquistion. Proceedings of 8th european knowledge acquisition workshop (EKAW ‘94). Lecture Notes in Artificial Intelligence 867. Hoegaarden: Springer-Verlag, 1994, pp. 1–25.
32.
KamišalićARiañoDWelzerT.Generating macro-temporality in timed transition diagrams. In: K4CARE: AIME workshop on knowledge management for health care procedures. Springer LNAI 4924, Amsterdam, The Netherlands, 7 July 2007, pp.62–74.
33.
HenzingerTAMannaZPnueliA.Timed transition systems. In: REX 1991: Real-time: Theory in practice. LNCS 600, Mook, The Netherlands, 3–7June1991, pp.226–251.
ByrneTJCampeanFNeaguD. Towards a framework for engineering big data: an automotive systems perspective. In: International design conference – design 2018, Dubrovnik, Croatia, 21–24 May 2018, pp.1511–1522.
45.
EzhilarasuCMSkafZJennionsIK.The application of reasoning to aerospace integrated vehicle health management (IVHM): challenges and opportunities. Prog Aerosp Sci2019; 105: 60–73.
46.
TaheriEKolmanovskyHGusikhinO.Survey of prognostics methods for condition-based maintenance in engineering systems, https://arxiv.org/pdf/1912.02708.pdf (2019, accessed 10 August 2021).
