Expert Systems and knowledge management for failure prediction to onshore pipelines: issue to Industry 4.0 implementation

Buccieri, Gilberto Pachoal; Muniz Jr., Jorge; Balestieri, José Antonio Perrella; Matelli, José Alexandre

doi:10.1590/0104-530X5771-20

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Sumário

Thematic Section: Digital Transformation, Intelligent Manufacturing and Supply Chain Management 4.0 • Gest. Prod. 27 (3) • 2020 • https://doi.org/10.1590/0104-530X5771-20 copy

Expert Systems and knowledge management for failure prediction to onshore pipelines: issue to Industry 4.0 implementation

Sistemas especialistas e gestão do conhecimento para previsão de falhas em dutos terrestres: uma questão para a implementação da Indústria 4.0.

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract:

The paper aims to propose an Expert System to predict the failure of onshore pipelines. Knowledge Management supports expertise sharing throughout the organization. The Expert System Prototype Model proposed is classified as Empirical Descriptive Research, and may support maintenance management. The findings evidence that Expert System proposed grounded in employee knowledge may be considered a promising solution to support Industry 4.0 implementation. The Expert System facilitates the decision-making of the experts so that the employees’ expertise can be better used in the implementation of Industry 4.0 and to face the new challenges related to the daily work in the organization. In this context, the Expert System can be considered as an innovative approach to manage maintenance processes and supporting reliable, consistent decisions during I.4 implementation.

Keywords:
Expert system; Knowledge management; Industry 4.0; Maintenance

Author(s)	Application Field	Research methodology	Objective
2	Financial	Empirical	Evaluation of the usefulness of several situations of the decision-maker responsibility and to suggest how to deal with them on a stand-alone basis.
10	Medicine	Theoretical	Development and exploration of a method to construct an ES without an explicit knowledge base.
15	Agriculture	Empirical	Investigation on ES projects from a knowledge transfer perspective.
20	Construction	Empirical	Tool development for flexible construction site management in repetitive projects.
24	Medicine	Theoretical	Tool development to extract the tacit knowledge consistently, transforming different types of knowledge into a composite and integrated knowledge base.
26	Engineering	Empirical	Evaluation of pharmacy students’ communication competence to face some companies' challenges in terms of product recall process management.
27	Education	Theoretical	Tool development for diagnosis, dynamic composition, and personalized knowledge retrieval.
29	Education	Empirical	Tool development to address the challenges in the timetabling problem of a university.
31	Medicine	Theoretical	Theoretical model development for acquiring and representing tacit knowledge to increase and sustain the competitive advantages of knowledge-intensive organizations.
41	Insurance	Theoretical	A tool development to build ES based on the web, which permits the expert to define the knowledge without having to know anything about AI.
42	Medicine	Empirical	Model development for computer-based medical diagnoses of primary immunodeficiency.
43	Financial	Empirical	Tool development to assist in managing an expert’s decision knowledge in evaluating an enterprise’s financial health.
47	Environmental	Empirical	Evaluation, categorization, and selection of the KM tools and techniques used in forest management to support decision-makers in several problem phases (identification, modeling and solving).

Category	Variable	I/O	Value¹	Column (Figure 2)	Category	Variable	I/O	Value¹ 1 Input variables can assume values yes (1), no (0) or any non-Boolean value (*). 2Fluids: ammonia, distillated, gas, isobutane, oil. 3Standard diameter: 3”; 4”; 6”; 8”; 10”; 12”; 14”; 16”, 18”, 20”, 24”, 30”, 48”.	Column (Figure 2)
General information	Operation time	I	^*	B	Fracture characteristics (cont.)	Secondary cracks	I	0/1	II
	Transported fluid2	I	*	C		Single crack	I	0/1	JJ
	Operation pressure	I	*	D		Single source	I	0/1	KK
	Operation temperature	I	*	E		Straight cracks	I	0/1	LL
	Failure date	I	*	F		Tree texture	I	0/1	MM
	Pipe diameter3	I	*	G	Adequate welding	Yes	I	0/1	NN
	Thickness	I	*	H	Adequate welding	No	I	0/1	OO
	Material grade	I	*	I	Cathodic protection	Yes	I	0/1	PP
	Welding type	I	*	J	Cathodic protection	No	I	0/1	QQ
					Crossing water streams	Yes	I	0/1	RR
General surface conditions	Dented	I	0/1	K		No	I	0/1	SS
	Cracked	I	0/1	L		Coating type	I	0/1	TT
	Corroded	I	0/1	M	Recommended tests	Micrography	O	0/1	UU
	Pitted	I	0/1	N		Microhardness	O	0/1	VV
	Fractured	I	0/1	O		SEM	O	0/1	WW
	Blistered	I	0/1	P		Chemical analysis	O	0/1	XX
	Perforated	I	0/1	Q		Impact	O	0/1	YY
	Holed	I	0/1	R		Metal base strain	O	0/1	ZZ
Coating state	Degraded	I	0/1	S		Welding stress	O	0/1	AAA
	No defect	I	0/1	T		Saline chamber	O	0/1	BBB
	Coated	I	0/1	U		X-ray	O	0/1	CCC
Failure surface condition	Metal loss	I	0/1	W		Thickness measure	O	0/1	DDD
	Blistered	I	0/1	V		Hardness	O	0/1	EEE
	Slotted	I	0/1	X		Metallography	O	0/1	FFF
	Cracks pattern	I	0/1	Y		Ultrasonic inspect	O	0/1	GGG
	Polished	I	0/1	Z		Magnetic particles	O	0/1	HHH
	Abrasion	I	0/1	AA		Penetrating liquid	O	0/1	III
Fracture characteristics	Beach marks	I	0/1	BB		Other	O	0/1	JJJ
	Branching crack	I	0/1	CC	Failure mechanism	Extern corrosion	O	2	A
	Chevron marks	I	0/1	DD		Intern corrosion	O	3	A
	Multiple cracks	I	0/1	EE		Third parties accident	O	4	A
	Multiple origins	I	0/1	FF		Erosion	O	5	A
	Oxide layer	I	0/1	GG		Material selection	O	6	A
	Plastic deformation	I	0/1	HH		Vandalism	O	7	A

Universidade Federal de São Carlos Departamento de Engenharia de Produção , Caixa Postal 676 , 13.565-905 São Carlos SP Brazil, Tel.: +55 16 3351 8471 - São Carlos - SP - Brazil
E-mail: gp@dep.ufscar.br

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[TFN1] ¹
Input variables can assume values yes (1), no (0) or any non-Boolean value (*). ²Fluids: ammonia, distillated, gas, isobutane, oil. ³Standard diameter: 3”; 4”; 6”; 8”; 10”; 12”; 14”; 16”, 18”, 20”, 24”, 30”, 48”.