State estimation based on zonotope

Wang, Ziyun; Wang, Yan; Ji, Zhicheng

doi:10.1201/b23146-3

Cited by 2 publications

(2 citation statements)

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“…Research work on diagnosability can be divided into two aspects: diagnosability evaluation [2][3][4] and diagnosability design [5,6]. Diagnosability evaluation refers to the process of measuring the degree to which system faults can be identified deterministically and efficiently, mainly revealing how difficult the fault can be diagnosed.…”

Section: Introductionmentioning

confidence: 99%

A Diagnosability-Integrated Design Approach Based on Graph Theory

Lv,

Shi

2023

Applied Sciences

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In order to take into account the influence of both system structure and diagnosis algorithm in the diagnosability design of the system, a diagnosability-integrated design method based on graph theory was proposed in this paper. Firstly, based on the diagnosability evaluation results, the difficulty of fault diagnosis was qualitatively analyzed using the K-means method, and the diagnosis plot of measurement point was drawn based on the analysis results. Secondly, the Bron–Kerbosch algorithm was used to extract the maximal cliques from the diagnosis plot of measurement point and determine the set of maximal cliques that can diagnose faults in the system based on the hypergraph edge coverage theorem. Finally, a cascade classifier was set on the maximal clique set to classify and identify faults in the system, and the performance of the diagnosis scheme was evaluated using the posterior probabilities of the classifier outputs combined with the Shannon entropy. At the same time, the method incorporated a measurement point update mechanism, which can decide whether to add additional measurement point according to the evaluation results of Shannon entropy to ensure better diagnosis effect. The results of simulation experiments showed that the fault diagnosis scheme designed by the method of this paper improved the correct rate of diagnosis results by 3.25 percentage points compared with other diagnosis schemes due to the simultaneous consideration of the structure of the system and the diagnosis method, and the diagnosis results of this paper were relatively stable in repeated experiments, which proved the practicality and effectiveness of the method of this paper.

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Section: Introductionmentioning

confidence: 99%

A Diagnosability-Integrated Design Approach Based on Graph Theory

Lv,

Shi

2023

Applied Sciences

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“…Consequently, this approach provides stringent bounds on parameter estimates. The SMI method can be categorized into various algorithms according to obtained SS, including ellipsoid method [17][18][19], box and polyhedron algorithm [20,21], interval estimation algorithm [22][23][24], and centrosymmetric polycell body algorithm [25]. The ellipsoid algorithm, renowned for its simplicity and computational efficiency, is often favored by researchers when dealing with optimization problems characterized by high computational complexity and difficulties in finding the optimal solution set.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive-Noise-Bound-Based Set-Membership Method for Process Identification of Industrial Control Loops

Wang,

Zhang

2023

Processes

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Modeling of key variable data needs to consider the complex characteristics of systems in the catalytic cracking unit (CCU) of petroleum refining process, such as slow time-varying behavior, complex dynamic properties, distributed traits, and unknown stochastic noise. To fully capture the dynamics of a linear ordinary dynamic process without introducing incremental components, an adaptive-noise-bound-based set-membership method (RSMI) is proposed in this paper. Under the set-membership framework, the output set is typically represented as an ellipsoid based on the assumed conditions. Firstly, a CARMA model is considered; longer-duration historical data are selected to capture the intricate dynamic characteristics of industrial control loops. Secondly, RSMI introduces am approach to determine allowance factor, optimizing the noise bound for better suitability in real-world noise environments. The adaptive noise bound is achieved by designing an optimization algorithm that seeks the optimal parameters within the optimization framework. The stability of the RSMI algorithm is demonstrated through the application of the Lyapunov method. Next, the RSMI algorithm has been applied in engineering practice and designed for offline and online training stages of control processes. Finally, simulation experiments are performed to model and predict real-time data of flow, pressure, and liquid-level control loops within a catalytic cracking unit. Furthermore, the effectiveness of the RSMI algorithm is validated through two general examples, and frequency domain analysis is performed.

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State estimation based on zonotope

Cited by 2 publications

References 0 publications

A Diagnosability-Integrated Design Approach Based on Graph Theory

A Diagnosability-Integrated Design Approach Based on Graph Theory

An Adaptive-Noise-Bound-Based Set-Membership Method for Process Identification of Industrial Control Loops

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