New health-state assessment model based on belief rule base with interpretability

Zhou, Zhijie; Cao, You; Hu, Guanyu; Zhang, Youmin; Tang, Shuaiwen; Chen, Yuan

doi:10.1007/s11432-020-3001-7

Cited by 38 publications

(29 citation statements)

References 24 publications

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“…Belief rules can provide a clear semantic description between the input and output of the PV power generation system, which is the main manifestation of the interpretability of the DBRB-I model 25 . Expert knowledge can be introduced into the model as a parameter by belief rules.…”

Section: The Dbrb-i Model Interpretabilitymentioning

confidence: 99%

“…Thus, in a PV power generation system, to ensure the reasonableness of the prediction results, the interpretability constraints are as follows: where is the interpretability constraint in the kth rule. For different systems, the interpretability constraints will be different, but it should be noted that the operating mechanism and common sense of the actual system need to be satisfied 25 . Moreover, a reasonable belief distribution shape should be monotonic or convex.…”

Section: The Dbrb-i Model Interpretabilitymentioning

confidence: 99%

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Deep belief rule based photovoltaic power forecasting method with interpretability

Han

Cao

et al. 2022

Sci Rep

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Accurate prediction of photovoltaic (PV) output power is of great significance for reasonable scheduling and development management of power grids. In PV power generation prediction system, there are two problems: the uncertainty of PV power generation and the inexplicability of the prediction result. The belief rule base (BRB) is a rule-based modeling method and can deal with uncertain information. Moreover, the modeling process of BRB has a certain degree of interpretability. However, rule explosion and the inexplicability of the optimized model limit the modeling ability of BRB in complex systems. Thus, a PV output power prediction model is proposed based on a deep belief rule base with interpretability (DBRB-I). In the DBRB-I model, the deep BRB structure is constructed to solve the rule explosion problem, and inefficient rules are simplified by a sensitivity analysis of the rules, which reduces the complexity of the model. Moreover, to ensure that the interpretability of the model is not destroyed, a new optimization method based on the projection covariance matrix adaptation evolution strategy (P-CMA-ES) algorithm is designed. Finally, a case study of the prediction of PV output power is conducted to illustrate the effectiveness of the proposed method.

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Section: The Dbrb-i Model Interpretabilitymentioning

confidence: 99%

Section: The Dbrb-i Model Interpretabilitymentioning

confidence: 99%

Deep belief rule based photovoltaic power forecasting method with interpretability

Han

Cao

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

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“…30 (2) It is difficult to guarantee that the generated solution does not leave the feasible region because of many strong equality constraints in the FEM-BRB model. 7 P-CMA-ES is an intelligent optimization algorithm proposed by Zhou et al, 40 which performs well in solving the nonlinear and nonconvex continuous optimization problem. The P-CMA-ES algorithm can make the generated solution always meet the equality constraints through projection operation, and can realize the convergence to the global optimal solution in a short time with fewer individuals.…”

Section: Remarkmentioning

confidence: 99%

“…In current studies, there are three types of models for the structural safety assessment. 7 (1) The white box model. This model can provide a transparent modeling process and interpretable results by establishing accurate mathematical models, 8 such as failure mode and effects analysis (FMEA), 9 fault tree analysis, 10 differential equation analysis 11 and the finite element method 12 (FEM).…”

Section: Introductionmentioning

confidence: 99%

“…21 As mentioned above, it is difficult to accurately assess the structural safety of LLT by using the black box model with insufficient fault samples. 7,22 Although the white box model can effectively use the principle knowledge to solve the problem of lack of fault samples, it is normally difficult to establish a precise analytical model. 8 The gray box model can integrate the advantages of the white and black models, use the qualitative and quantitative information, and deal with uncertainties in the LLT system.…”

Section: Introductionmentioning

confidence: 99%

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A novel structural safety assessment method of large liquid tank based on the belief rule base and finite element method

Chen

Zhou

Yang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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The structural safety assessment of large liquid tanks (LLT) has attracted an extensive attention. As a typical gray box model, the belief rule base (BRB) model can handle qualitative information and quantitative data simultaneously, which is a suitable modeling tool for structural safety assessment. However, it is difficult to establish and train the BRB model when there is a lack of expert experience and fault samples of LLT. Therefore, a novel safety assessment model for LLT based on BRB and finite element method (FEM-BRB) is proposed in this paper. The FEM is introduced to construct the BRB model by combining expert knowledge and industry standards for the first time, which can effectively compensate for the lack of expert experience. The fault samples are generated in the mechanism simulation model under different working conditions. Based on the fault samples generated by the FEM and historical samples, the projection covariance matrix adaption evolution strategy (P-CMA-ES) optimization algorithm is then used to train the model, which further improves the structural safety assessment accuracy when lacking fault samples. A case study of three actual oil tanks in a coastal port is conducted to illustrate the effectiveness and advantage of the developed structural safety assessment method.

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A new belief rule base based distributed online fault diagnosis method for multi‐agent systems

Wang

Yang

et al. 2023

Asian Journal of Control

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In this paper, based on belief rule base (BRB), we studied the distributed online fault diagnosis problem of multi‐agent systems (MASs). To begin with, considering the distributed nature of MASs, by using the relative information between an agent and its neighbors, a distributed online fault diagnosis model is constructed. Next, for the results of fault diagnosis, when an agent doesn't get enough relative information to diagnose the fault, the fault diagnosis result may be inaccurate, and if there exist too many neighbors of an agent, the number of attributes of the proposed BRB fault diagnosis model will be too large, which will lead to large computation. To improve the performance of the proposed distributed online fault diagnosis model, a real‐time communication topology switching strategy was proposed. Under the constructed distributed fault diagnosis model, the real‐time performance and the accuracy of fault diagnosis for agents can be guaranteed simultaneously. Finally, a simulation is given to validate the effectiveness and advancement of the distributed online fault diagnosis method proposed in this paper.

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New health-state assessment model based on belief rule base with interpretability

Cited by 38 publications

References 24 publications

Deep belief rule based photovoltaic power forecasting method with interpretability

Deep belief rule based photovoltaic power forecasting method with interpretability

A novel structural safety assessment method of large liquid tank based on the belief rule base and finite element method

A new belief rule base based distributed online fault diagnosis method for multi‐agent systems

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