Novel ARMF Integrated with Improved LSDA and Its Application in Fault Diagnosis

Qun Xiong Zhu; Ning Zhang; Yan Lin He; Yuan Xu

doi:10.1109/DDCLS55054.2022.9858537

Novel ARMF Integrated with Improved LSDA and Its Application in Fault Diagnosis

Qun Xiong Zhu, Ning Zhang, Yan Lin He, Yuan Xu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

The industrial process is developing to be intelligent and complex, thus the process data presents high dimension, nonlinearity and highly coupled features. Facing these features, this paper proposes a signal pattern-matching fault diagnosis method based on the adaptive rank-order morphological filter (ARMF) integrated with improved locality sensitive discriminant analysis (LSDA) named ILSDA-ARMF. This proposed methodology first fully extracts the variable features related to the fault using the improved LSDA; then the data after dimensionality reduction (DR) is used for signal pattern matching by using ARMF to achieve fault classification. The main advantage of the improved LSDA is that the Mahalanobis distance considers the correlation between samples and their nearest neighbor points. Meanwhile, the Tennessee Eastman (TE) chemical process is experimented with to verify the performance of the proposed ILSDA-ARMF. The simulation results show that the method proposed in this paper achieves more satisfactory results compared with other related methods.

Original language	English
Title of host publication	Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022
Editors	Mingxuan Sun, Zengqiang Chen
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	414-419
Number of pages	6
ISBN (Electronic)	9781665496759
DOIs	https://doi.org/10.1109/DDCLS55054.2022.9858537
Publication status	Published - 2022
Externally published	Yes
Event	11th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2022 - Emeishan, China Duration: 3 Aug 2022 → 5 Aug 2022

Publication series

Name	Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022

Conference

Conference	11th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2022
Country/Territory	China
City	Emeishan
Period	3/08/22 → 5/08/22

Keywords

Adaptive Rank-order Morphological Filter
Fault Diagnosis
Locality Sensitive Discriminant Analysis
Signal Pattern Matching
Tennessee Eastman

Access to Document

10.1109/DDCLS55054.2022.9858537

Cite this

Zhu, Q. X., Zhang, N., He, Y. L., & Xu, Y. (2022). Novel ARMF Integrated with Improved LSDA and Its Application in Fault Diagnosis. In M. Sun, & Z. Chen (Eds.), Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022 (pp. 414-419). (Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DDCLS55054.2022.9858537

Zhu, Qun Xiong ; Zhang, Ning ; He, Yan Lin et al. / Novel ARMF Integrated with Improved LSDA and Its Application in Fault Diagnosis. Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022. editor / Mingxuan Sun ; Zengqiang Chen. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 414-419 (Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022).

@inproceedings{facba4f0a79f4875ba7e60d5d5021818,

title = "Novel ARMF Integrated with Improved LSDA and Its Application in Fault Diagnosis",

abstract = "The industrial process is developing to be intelligent and complex, thus the process data presents high dimension, nonlinearity and highly coupled features. Facing these features, this paper proposes a signal pattern-matching fault diagnosis method based on the adaptive rank-order morphological filter (ARMF) integrated with improved locality sensitive discriminant analysis (LSDA) named ILSDA-ARMF. This proposed methodology first fully extracts the variable features related to the fault using the improved LSDA; then the data after dimensionality reduction (DR) is used for signal pattern matching by using ARMF to achieve fault classification. The main advantage of the improved LSDA is that the Mahalanobis distance considers the correlation between samples and their nearest neighbor points. Meanwhile, the Tennessee Eastman (TE) chemical process is experimented with to verify the performance of the proposed ILSDA-ARMF. The simulation results show that the method proposed in this paper achieves more satisfactory results compared with other related methods.",

keywords = "Adaptive Rank-order Morphological Filter, Fault Diagnosis, Locality Sensitive Discriminant Analysis, Signal Pattern Matching, Tennessee Eastman",

author = "Zhu, {Qun Xiong} and Ning Zhang and He, {Yan Lin} and Yuan Xu",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 11th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2022 ; Conference date: 03-08-2022 Through 05-08-2022",

year = "2022",

doi = "10.1109/DDCLS55054.2022.9858537",

language = "English",

series = "Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "414--419",

editor = "Mingxuan Sun and Zengqiang Chen",

booktitle = "Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022",

address = "United States",

}

Zhu, QX, Zhang, N, He, YL & Xu, Y 2022, Novel ARMF Integrated with Improved LSDA and Its Application in Fault Diagnosis. in M Sun & Z Chen (eds), Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022. Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022, Institute of Electrical and Electronics Engineers Inc., pp. 414-419, 11th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2022, Emeishan, China, 3/08/22. https://doi.org/10.1109/DDCLS55054.2022.9858537

Novel ARMF Integrated with Improved LSDA and Its Application in Fault Diagnosis. / Zhu, Qun Xiong; Zhang, Ning; He, Yan Lin et al.
Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022. ed. / Mingxuan Sun; Zengqiang Chen. Institute of Electrical and Electronics Engineers Inc., 2022. p. 414-419 (Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Novel ARMF Integrated with Improved LSDA and Its Application in Fault Diagnosis

AU - Zhu, Qun Xiong

AU - Zhang, Ning

AU - He, Yan Lin

AU - Xu, Yuan

PY - 2022

Y1 - 2022

N2 - The industrial process is developing to be intelligent and complex, thus the process data presents high dimension, nonlinearity and highly coupled features. Facing these features, this paper proposes a signal pattern-matching fault diagnosis method based on the adaptive rank-order morphological filter (ARMF) integrated with improved locality sensitive discriminant analysis (LSDA) named ILSDA-ARMF. This proposed methodology first fully extracts the variable features related to the fault using the improved LSDA; then the data after dimensionality reduction (DR) is used for signal pattern matching by using ARMF to achieve fault classification. The main advantage of the improved LSDA is that the Mahalanobis distance considers the correlation between samples and their nearest neighbor points. Meanwhile, the Tennessee Eastman (TE) chemical process is experimented with to verify the performance of the proposed ILSDA-ARMF. The simulation results show that the method proposed in this paper achieves more satisfactory results compared with other related methods.

AB - The industrial process is developing to be intelligent and complex, thus the process data presents high dimension, nonlinearity and highly coupled features. Facing these features, this paper proposes a signal pattern-matching fault diagnosis method based on the adaptive rank-order morphological filter (ARMF) integrated with improved locality sensitive discriminant analysis (LSDA) named ILSDA-ARMF. This proposed methodology first fully extracts the variable features related to the fault using the improved LSDA; then the data after dimensionality reduction (DR) is used for signal pattern matching by using ARMF to achieve fault classification. The main advantage of the improved LSDA is that the Mahalanobis distance considers the correlation between samples and their nearest neighbor points. Meanwhile, the Tennessee Eastman (TE) chemical process is experimented with to verify the performance of the proposed ILSDA-ARMF. The simulation results show that the method proposed in this paper achieves more satisfactory results compared with other related methods.

KW - Adaptive Rank-order Morphological Filter

KW - Fault Diagnosis

KW - Locality Sensitive Discriminant Analysis

KW - Signal Pattern Matching

KW - Tennessee Eastman

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U2 - 10.1109/DDCLS55054.2022.9858537

DO - 10.1109/DDCLS55054.2022.9858537

M3 - Conference contribution

AN - SCOPUS:85137813353

T3 - Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022

SP - 414

EP - 419

BT - Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022

A2 - Sun, Mingxuan

A2 - Chen, Zengqiang

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 11th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2022

Y2 - 3 August 2022 through 5 August 2022

ER -

Zhu QX, Zhang N, He YL, Xu Y. Novel ARMF Integrated with Improved LSDA and Its Application in Fault Diagnosis. In Sun M, Chen Z, editors, Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 414-419. (Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022). doi: 10.1109/DDCLS55054.2022.9858537

Novel ARMF Integrated with Improved LSDA and Its Application in Fault Diagnosis

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