Industrial Process Fault Detection Based on Incremental Isometric Mapping and Double Local Density Method

doi:10.16183/j.cnki.jsjtu.2022.423

Abstract

Abstract:

To address the nonlinearity and dynamics of industrial processes, an incremental isometric mapping (IISOMAP) in combination with double local density (DLD) is proposed as a fault detection method (IISOMAP-DLD) based on stream shape learning. First, IISOMAP is used to map the raw data into a low-dimensional manifold feature subspace and a residual subspace. Then, the double local density method is introduced in the two subspaces respectively to construct statistics to monitor the process. Finally, the IISOMAP-DLD method is applied to the Tennessee-Eastman (TE) process, and the experimental results show that IISOMAP-DLD has a higher fault detection rate than the other methods. IISOMAP preserves the intrinsic characteristics of the data and solves the nonlinear problems of the process, while the double local density method can eliminate the dynamic of the process.

Key words: manifold learning, isometric mapping (ISOMAP), local density, fault detection, dynamic

CLC Number:

TP277

FENG Liwei, SUN Liwen, GU Huan, LI Yuan. Industrial Process Fault Detection Based on Incremental Isometric Mapping and Double Local Density Method[J]. Journal of Shanghai Jiao Tong University, 2024, 58(4): 525-533.

Figures/Tables 8

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Tab.1

Fault detection rate of each method in TE process

组别	KPCA		DKPCA		WKNN D/%	KSFA		IISOMAP-DLD
组别	T²/%	SPE/%	T²/%	SPE/%	WKNN D/%	T²/%	$T_{e}^{2}$ $T e 2$ /%	τ/%	τ_e/%
f1	98.86	99.14	98.54	99.43	98.86	99.00	98.57	99.14	99.54
f2	94.01	94.86	92.44	94.15	96.57	97.00	94.86	99.00	99.54
f3	0.43	15.55	0.57	1.41	20.86	25.00	52.29	69.00	19.86
f4	99.71	99.71	100	100	99.86	99.86	99.86	100	99.86
f5	0	30.10	0	0.70	37.71	42.00	57.71	73.71	14.71
f6	99.71	99.71	100	100	99.86	99.86	99.86	100	99.86
f7	99.71	99.71	100	100	99.86	99.86	99.86	100	99.86
f8	86.88	89.87	85.84	89.84	88.43	88.71	88.71	90.00	86.00
f9	3.42	17.26	1.57	3.34	22.00	17.71	47.00	62.14	15.57
f10	85.16	89.30	83.12	84.55	88.14	90.86	87.29	90.43	83.00
f11	96.86	98.43	94.13	98.00	97.86	98.14	92.00	98.71	95.29
f12	36.09	80.88	24.75	46.35	70.86	60.57	73.29	79.71	42.57
f13	94.01	95.01	94.28	94.99	94	94.43	93.71	95.57	93.71
f14	98.57	98.72	98.71	98.86	98.71	98.71	91.43	99.00	98.00
f15	0	3.57	0	0	13.29	7.29	58.86	67.14	7.57
f16	1.28	1.28	1.29	1.00	0.86	0.71	0.71	29.86	16.43
f17	83.17	84.88	82.98	84.55	84.71	84.57	82.43	86.86	83.00
f18	49.07	63.62	49.50	58.08	56.86	55.57	59.86	68.86	62.00
f19	91.87	95.72	89.56	94.42	94.57	96.00	94.43	96.71	95.14
f20	84.88	85.16	84.98	85.41	84.29	84.43	81.86	85.14	83.71
f21	1.43	1.28	1.43	1.00	0.71	0.71	1.41	29.29	15.86

Tab.1

Fig.7

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