A hierarchical scheme for dynamic monitoring of multi-scale multi-mode systems

Jiaorao Wang; Lishuai Li; S. Joe Qin

doi:10.1016/j.compchemeng.2025.109107

A hierarchical scheme for dynamic monitoring of multi-scale multi-mode systems

Jiaorao Wang, Lishuai Li, S. Joe Qin

Department of Data Science

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

17 Downloads (CityUHK Scholars)

Abstract

methods typically address either multi-scale, dynamic, or multi-mode monitoring separately. This paper proposes a general hierarchical scheme for dynamic monitoring of systems with multi-mode dynamic behaviors. The core strength of the proposed method lies in its iterative procedure, which comprises two steps: dynamic pattern modeling and mode segmentation. Firstly, dynamic patterns across different modes are captured using latent vector autoregressive (LaVAR) modeling. In mode segmentation, data representing new dynamic patterns are filtered for the construction of the next LaVAR model, guided by two monitoring indices. The hierarchical structure sequentially extracts dynamic patterns, inherently dealing with unbalanced data common in industrial applications. Experiments are conducted to demonstrate the effectiveness of the proposed scheme for multi-mode dynamic system monitoring. © 2025 Published by Elsevier Ltd.

Original language	English
Article number	109107
Journal	Computers & Chemical Engineering
Volume	198
Online published	30 Mar 2025
DOIs	https://doi.org/10.1016/j.compchemeng.2025.109107
Publication status	Published - Jul 2025

Funding

This work was partially supported by a grant from a General Research Fund from the Research Grants Council (RGC) of Hong Kong SAR, China (Project No. 11303421) and a grant from the ITF-Guangdong-Hong Kong Technology Cooperation Funding Scheme (Project Ref. No. GHP/145/20). The authors thank the editor and anonymous reviewers for their constructive comments that led to a substantially improved article.

Research Keywords

Multi-scale multi-mode systems
Complex system monitoring
Dynamic modeling
Latent variable modeling

Publisher's Copyright Statement

This full text is made available under CC-BY-NC 4.0. https://creativecommons.org/licenses/by-nc/4.0/

Access Output

10.1016/j.compchemeng.2025.109107

276816785.pdfFinal Published version, 4.78 MBLicence: CC BY-NC

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

1 Finished
1 Active

ITF: Aerospace System Prognostics and Health Management Model and Telemetry Task Optimization
LI, L. (Principal Investigator / Project Coordinator) & QIN, S. Z. J. (Co-Investigator)
1/09/22 → …
Project: Research
GRF: Dimension Reduction Modeling Methods for High Dimensional Dynamic Data in Smart Manufacturing and Operations
QIN, S. J. (Principal Investigator / Project Coordinator)
1/09/21 → 6/07/23
Project: Research

Cite this

@article{05a2cbb2b9064ee38351ab7e4b59ca7a,

title = "A hierarchical scheme for dynamic monitoring of multi-scale multi-mode systems",

abstract = "methods typically address either multi-scale, dynamic, or multi-mode monitoring separately. This paper proposes a general hierarchical scheme for dynamic monitoring of systems with multi-mode dynamic behaviors. The core strength of the proposed method lies in its iterative procedure, which comprises two steps: dynamic pattern modeling and mode segmentation. Firstly, dynamic patterns across different modes are captured using latent vector autoregressive (LaVAR) modeling. In mode segmentation, data representing new dynamic patterns are filtered for the construction of the next LaVAR model, guided by two monitoring indices. The hierarchical structure sequentially extracts dynamic patterns, inherently dealing with unbalanced data common in industrial applications. Experiments are conducted to demonstrate the effectiveness of the proposed scheme for multi-mode dynamic system monitoring. {\textcopyright} 2025 Published by Elsevier Ltd.",

keywords = "Multi-scale multi-mode systems, Complex system monitoring, Dynamic modeling, Latent variable modeling",

author = "Jiaorao Wang and Lishuai Li and {Joe Qin}, S.",

year = "2025",

month = jul,

doi = "10.1016/j.compchemeng.2025.109107",

language = "English",

volume = "198",

journal = "Computers & Chemical Engineering",

issn = "0098-1354",

publisher = "Elsevier B.V.",

}

TY - JOUR

T1 - A hierarchical scheme for dynamic monitoring of multi-scale multi-mode systems

AU - Wang, Jiaorao

AU - Li, Lishuai

AU - Joe Qin, S.

PY - 2025/7

Y1 - 2025/7

N2 - methods typically address either multi-scale, dynamic, or multi-mode monitoring separately. This paper proposes a general hierarchical scheme for dynamic monitoring of systems with multi-mode dynamic behaviors. The core strength of the proposed method lies in its iterative procedure, which comprises two steps: dynamic pattern modeling and mode segmentation. Firstly, dynamic patterns across different modes are captured using latent vector autoregressive (LaVAR) modeling. In mode segmentation, data representing new dynamic patterns are filtered for the construction of the next LaVAR model, guided by two monitoring indices. The hierarchical structure sequentially extracts dynamic patterns, inherently dealing with unbalanced data common in industrial applications. Experiments are conducted to demonstrate the effectiveness of the proposed scheme for multi-mode dynamic system monitoring. © 2025 Published by Elsevier Ltd.

AB - methods typically address either multi-scale, dynamic, or multi-mode monitoring separately. This paper proposes a general hierarchical scheme for dynamic monitoring of systems with multi-mode dynamic behaviors. The core strength of the proposed method lies in its iterative procedure, which comprises two steps: dynamic pattern modeling and mode segmentation. Firstly, dynamic patterns across different modes are captured using latent vector autoregressive (LaVAR) modeling. In mode segmentation, data representing new dynamic patterns are filtered for the construction of the next LaVAR model, guided by two monitoring indices. The hierarchical structure sequentially extracts dynamic patterns, inherently dealing with unbalanced data common in industrial applications. Experiments are conducted to demonstrate the effectiveness of the proposed scheme for multi-mode dynamic system monitoring. © 2025 Published by Elsevier Ltd.

KW - Multi-scale multi-mode systems

KW - Complex system monitoring

KW - Dynamic modeling

KW - Latent variable modeling

U2 - 10.1016/j.compchemeng.2025.109107

DO - 10.1016/j.compchemeng.2025.109107

M3 - RGC 21 - Publication in refereed journal

SN - 0098-1354

VL - 198

JO - Computers & Chemical Engineering

JF - Computers & Chemical Engineering

M1 - 109107

ER -

A hierarchical scheme for dynamic monitoring of multi-scale multi-mode systems

Abstract

Funding

Research Keywords

Publisher's Copyright Statement

Access Output

Other Access Options

Permanent Link

Fingerprint

Projects

ITF: Aerospace System Prognostics and Health Management Model and Telemetry Task Optimization

GRF: Dimension Reduction Modeling Methods for High Dimensional Dynamic Data in Smart Manufacturing and Operations

Cite this