An approach to robust condition monitoring in industrial processes using pythagorean membership grades

RAMOS, ADRIÁN RODRÍGUEZ; LÁZARO, JOSÉ M. BERNAL DE; CORONA, CARLOS CRUZ; SILVA NETO, ANTÔNIO J. DA; LLANES-SANTIAGO, ORESTES

doi:10.1590/0001-3765202220200662

ADRIÁN RODRÍGUEZ RAMOS

design and implementation of algorithms, simulations, design of the datasets, analysis and interpretation of results, writing the draft article

Automation and Computing Department, Universidad Tecnológica de la Habana José Antonio Echeverría, CUJAE, calle 114, 11901, CUJAE, Marianao, CP 19390, La Habana, Cuba

http://orcid.org/0000-0002-0240-7491

JOSÉ M. BERNAL DE LÁZARO

initial simulations, analysis and interpretation of results

Automation and Computing Department, Universidad Tecnológica de la Habana José Antonio Echeverría, CUJAE, calle 114, 11901, CUJAE, Marianao, CP 19390, La Habana, Cuba

http://orcid.org/0000-0002-2797-0205

CARLOS CRUZ CORONA

initial ideas, analysis and interpretation of results.

Department of Computer Science and Artificial Intelligence, Faculty, E.T.S. Ingeniería Informática, Universidad de Granada, calle Periodista Saucedo Aranda S/N, CP 18071, Granada, España

http://orcid.org/0000-0003-2072-4949

ANTÔNIO J. DA SILVA NETO

initial ideas, analysis and interpretation of results

Instituto Politécnico-Universidade do Estado do Rio de Janeiro (IPRJ), Campus Nova Friburgo, Rua Bonfim, 25, Vila Amélia, 28.625.-570, Nova Friburgo, RJ, Brazil

http://orcid.org/0000-0002-9616-6093

ORESTES LLANES-SANTIAGO

initial ideas, algorithm design, analysis and interpretation of results, reviewing the draft article, preparing the final version

Automation and Computing Department, Universidad Tecnológica de la Habana José Antonio Echeverría, CUJAE, calle 114, 11901, CUJAE, Marianao, CP 19390, La Habana, Cuba

http://orcid.org/0000-0002-6864-9629

Correspondence to: Orestes Llanes-Santiago E-mail: orestes@tesla.cujae.edu.cu

Fault	Process variable	Type
F1	A/C feed ratio, B composition constant	step
F2	B composition, A/C ration constant	step
F6	A feed loss	step
F7	C header pressure loss-reduced availability	step

FCM				KFCM
	C1	C2	TA ( $%$ )		C1	C2	TA ( $%$ )
C1	115	135	46.0	C1	207	43	82.8
C2	141	109	43.6	C2	37	213	85.2
AVE			44.8	AVE			84.0

IFCM				KIFCM
	C1	C2	TA ( $%$ )		C1	C2	TA ( $%$ )
C1	121	129	48.4	C1	219	31	87.6
C2	132	118	47.2	C2	28	222	88.8
AVE			47.8	AVE			88.2

PyFCM				KPyFCM
	C1	C2	TA ( $%$ )		C1	C2	TA ( $%$ )
C1	129	121	51.6	C1	231	19	92.4
C2	118	132	52.8	C2	13	237	94.8
AVE			52.2	AVE			93.6

FCM				KFCM
	C1	C2	TA ( $%$ )		C1	C2	TA ( $%$ )
C1	128	122	51.2	C1	199	51	79.6
C2	135	115	46.0	C2	47	203	81.2
AVE			48.6	AVE			80.4

IFCM				KIFCM
	C1	C2	TA ( $%$ )		C1	C2	TA ( $%$ )
C1	134	116	53.6	C1	214	36	85.6
C2	126	124	49.6	C2	34	216	86.4
AVE			51.6	AVE			86.0

PyFCM				KPyFCM
	C1	C2	TA ( $%$ )		C1	C2	TA ( $%$ )
C1	139	111	55.6	C1	233	17	93.2
C2	124	126	50.4	C2	22	228	91.2
AVE			53.0	AVE			92.2

FCM				KFCM
	C1	C2	TA ( $%$ )		C1	C2	TA ( $%$ )
C1	80	95	45.71	C1	135	40	77.1
C2	92	83	47.4	C2	33	142	81.4
AVE			46.6	AVE			79.1

IFCM				KIFCM
	C1	C2	TA ( $%$ )		C1	C2	TA ( $%$ )
C1	84	91	48.0	C1	141	34	80.6
C2	86	89	50.9	C2	26	149	85.1
AVE			49.4	AVE			82.9

PyFCM				KPyFCM
	C1	C2	TA ( $%$ )		C1	C2	TA ( $%$ )
C1	89	86	50.9	C1	169	6	96.6
C2	84	91	52.0	C2	7	168	96.0
AVE			51.4	AVE			96.3

FCM				KFCM
	C1	C2	TA ( $%$ )		C1	C2	TA ( $%$ )
C1	116	134	46.4	C1	216	34	86.4
C2	129	121	48.4	C2	31	219	87.6
AVE			47.4	AVE			87.0

IFCM				KIFCM
	C1	C2	TA ( $%$ )		C1	C2	TA ( $%$ )
C1	121	129	48.4	C1	223	27	89.2
C2	122	128	51.2	C2	22	228	91.2
AVE			49.8	AVE			90.2

PyFCM				KPyFCM
	C1	C2	TA ( $%$ )		C1	C2	TA ( $%$ )
C1	131	119	52.4	C1	237	13	94.8
C2	116	134	53.6	C2	8	242	96.8
AVE			53.0	AVE			95.8

FCM						KFCM
	F1	F2	F6	F7	TA ( $%$ )		F1	F2	F6	F7	TA ( $%$ )
F1	495	211	109	145	51.56	F1	862	40	25	33	89.79
F2	153	551	77	179	57.40	F2	30	871	22	37	90.73
F6	225	68	530	137	55.21	F6	49	21	867	23	90.31
F7	244	115	92	509	53.02	F7	55	40	34	831	86.56
AVE					54.30	AVE					89.35

IFCM						KIFCM
	F1	F2	F6	F7	TA ( $%$ )		F1	F2	F6	F7	TA ( $%$ )
F1	668	153	47	92	69.58	F1	892	28	17	23	92.92
F2	129	701	55	75	73.02	F2	19	895	14	32	93.23
F6	138	62	689	71	71.77	F6	21	12	908	19	94.58
F7	140	107	89	624	65.00	F7	37	16	21	886	92.29
AVE					69.84	AVE					93.26

PyFCM						KPyFCM
	F1	F2	F6	F7	TA ( $%$ )		F1	F2	F6	F7	TA ( $%$ )
F1	695	163	56	100	72.40	F1	937	14	0	9	97.60
F2	134	730	69	85	76.04	F2	0	100	0	0	100.0
F6	148	72	717	79	74.69	F6	6	0	950	4	98.96
F7	145	127	101	651	67.89	F7	18	9	0	933	97.19
AVE					72.74	AVE					98.44

Algorithm	Time (seconds)
FCM	0.1678
IFCM	0.2203
PyFCM	0.2758
KFCM	0.6082
KIFCM	0.6634
KPyFCM	0.7005

Algorithm	No.Wins	Ranking
FCM	0	6
IFCM	1	5
PyFCM	2	4
KFCM	3	3
KIFCM	4	2
KPyFCM	5	1

[1] Correspondence to: Orestes Llanes-Santiago E-mail: orestes@tesla.cujae.edu.cu

Brasil

Brasil

An approach to robust condition monitoring in industrial processes using pythagorean membership grades

Abstract