Health condition estimation of spacecraft key components using belief rule base

Tang, Xilang; Xue-qi, Wang; Xiao, Mingqing; Yung, Kai Leung; Hu, Bin

doi:10.1080/17517575.2019.1670361

Cited by 10 publications

(6 citation statements)

References 48 publications

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“…In addition, in practice, battery systems often experience temperature variations during operation. Understanding how temperature variations, even subtle ones, interact with other factors that affect capacity degradation is critical to practical battery health assessment and predictive maintenance 29 . The battery capacity was chosen as the health indicator.…”

Section: Experimental Researchmentioning

confidence: 99%

A new state‐of‐health estimation method for Li‐ion batteries based on interpretable belief rule base with expert knowledge credibility

Yin,

Jia,

et al. 2023

Energy Science & Engineering

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State‐of‐health (SOH) estimation methods for Li‐ion batteries are important for the safe operation of the entire system. However, it is often challenging due to the uncertainty within the batteries and the criteria for model interpretability. Belief rule base (BRB) is a rule‐based expert system that has certain advantages for both aspects. However, several problems with BRB interpretability need to be solved urgently. First, expert knowledge credibility is often given subjectively, while objective information is neglected to be considered. Second, BRB interpretability is easily ignored or corrupted in the optimization process. Third, expert knowledge is assumed to be completely reliable information to be used as interpretability evaluation criterion. Therefore, a new SOH estimation method for Li‐ion batteries based on interpretable BRB with expert knowledge credibility (IBRB‐c) is proposed. In the IBRB‐c, the calculation method of expert knowledge credibility is given. Then, an optimization algorithm with interpretability strategies is used. Finally, the concept of the fuzzy interpretable interval is proposed to design the interpretable evaluation criterion. The effectiveness of the proposed method is verified by using the experiment of NASA Li‐ion battery as a case study.

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Section: Experimental Researchmentioning

confidence: 99%

A new state‐of‐health estimation method for Li‐ion batteries based on interpretable belief rule base with expert knowledge credibility

Yin,

Jia,

et al. 2023

Energy Science & Engineering

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“…When an unmanned space system was deployed, it was critical to monitor its health condition dynamically of critical components to ensure safe and normal operations. Tang et al [84] suggested using belief rules to assess health conditions of critical components in spacecraft; Shi et al [85] introduced an integrated approach for fault diagnosis of spacecraft. Song et al [86] discussed the correspondence of acquired data and the health conditions of spacecraft for the recognition of system health status.…”

Section: A Sensing Technologiesmentioning

confidence: 99%

The State of the Art of Information Integration in Space Applications

Yung

et al. 2022

IEEE Access

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This paper aims to present a comprehensive survey on information integration (II) in space informatics. With an ever-increasing scale and dynamics of complex space systems, II has become essential in dealing with the complexity, changes, dynamics, and uncertainties of space systems. The applications of space II (SII) require addressing some distinctive functional requirements (FRs) of heterogeneity, networking, communication, security, latency, and resilience; while limited works are available to examine recent advances of SII thoroughly. This survey helps to gain the understanding of the state of the art of SII in sense that (1) technical drivers for SII are discussed and classified; (2) existing works in space system development are analyzed in terms of their contributions to space economy, divisions, activities, and missions; (3) enabling space information technologies are explored at aspects of sensing, communication, networking, data analysis, and system integration; (4) the importance of first-time right (FTR) for implementation of a space system is emphasized, the limitations of digital twin (DT-I) as technological enablers are discussed, and a concept digital-triad (DT-II) is introduced as an information platform to overcome these limitations with a list of fundamental design principles; (5) the research challenges and opportunities are discussed to promote SII and advance space informatics in future.INDEX TERMS Information integration (II), space informatics, digital triad (DT-II), internet of digital-triad things (IoDTT), first-time right (FTR), space information integration (SII). FIGURE 2. Technical drivers of II applications.

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“…We choose the equal discharge voltage difference time interval (TIEDVD), the mean temperature (MT) during the discharge process [33], the mean voltage falloff (MVF) [34] and the discharge dump time (DDT) as characteristic features of the battery health indicator. We choose the battery capacity as the health indicator.…”

Section: ) Battery Dataset From Nasamentioning

confidence: 99%

“…The experiment stops when the battery capacity reaches 80% of the rated capacity. We choose the TIEDVD [33], the DDT and the MVF [34] as characteristic features. The capacity 9 degradation curve of the battery is shown in Fig.…”

Section: ) Battery Dataset From Calcementioning

confidence: 99%

A Fusion Prognostic Method for Remaining Useful Life Prediction Based on an Extended Belief Rule Base and Particle Filters

et al. 2021

Self Cite

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As a critical part of prognostics and health management (PHM), remaining useful life (RUL) prediction can provide manufacturers and users with system lifetime information and improve the reliability of maintainable systems. Particle filters (PFs) are powerful tools for RUL prediction because they can represent the uncertainty of results well. However, due to the lack of measurement data, the parameters of the measurement model cannot be updated during the long-term prediction process. Additionally, for complex systems, the measurement model of a system often cannot be obtained in an analytical form. In this paper, a fusion prognostic method based on an extended belief rule base (EBRB) and a PF is designed to solve these problems. In the proposed framework, a double-layer maximum mean discrepancy-extended belief rule base (DMMD-EBRB) model with time delay is adopted to estimate and predict the hidden behavior of a degrading system. The unknown parameters of the degradation model are identified by the PF using the output of the EBRB. Afterwards, the system state is further predicted by the PF. The effectiveness of the proposed method is validated with the NASA-PCoE and CALCE lithium-ion battery degradation experiment datasets. In addition, several other related fusion methods are investigated for comparison with the proposed method. The experiments show that the proposed method yields better performance than the existing methods.INDEX TERMS Maximum mean discrepancy, Extended belief rule base, Fusion prognostic approach, Particle filter, Prognostic and health management.

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Health condition estimation of spacecraft key components using belief rule base

Cited by 10 publications

References 48 publications

A new state‐of‐health estimation method for Li‐ion batteries based on interpretable belief rule base with expert knowledge credibility

A new state‐of‐health estimation method for Li‐ion batteries based on interpretable belief rule base with expert knowledge credibility

The State of the Art of Information Integration in Space Applications

A Fusion Prognostic Method for Remaining Useful Life Prediction Based on an Extended Belief Rule Base and Particle Filters

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