Resilience Metric of Equipment System: Theory, Measurement and Sensitivity Analysis

Yang, Bofan; Zhang, Lin; Zhang, Bo; Wang, Wenfeng; Zhang, Minglinag

doi:10.1016/j.ress.2021.107889

Cited by 15 publications

(5 citation statements)

References 49 publications

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“…They respectively represent the capability at one period of the process from potential disturbance to return to normal, while system resilience represents the capability over the whole life cycle. Thus, system resilience is not only the sum of reliability, robustness, and recoverability, but also the result that emerges from the combination of the three [ 39 ]. That is, resilience can be described by these three indexes, as shown in Fig 5 .…”

Section: Degradation Trend Estimationmentioning

confidence: 99%

A different method of fault feature extraction under noise disturbance and degradation trend estimation with system resilience for rolling bearings

et al. 2023

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Due to the effects of noise disturbances and system resilience, the current methods for rolling bearing fault feature extraction and degradation trend estimation can hardly achieve more satisfactory results. To address the above issues, we propose a different method for fault feature extraction and degradation trend estimation. Firstly, we preset the Bayesian inference criterion to evaluate the complexity of the denoised vibration signal. When its complexity reaches a minimum, the noise disturbances are exactly removed. Secondly, we define the system resilience obtained by the Bayesian network as the intrinsic index of the system, which is used to correct the equipment degradation trend obtained by the multivariate status estimation technique. Finally, the effectiveness of the proposed method is verified by the completeness of the extracted fault features and the accuracy of the degradation trend estimation over the whole life cycle of the bearing degradation data.

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Section: Degradation Trend Estimationmentioning

confidence: 99%

A different method of fault feature extraction under noise disturbance and degradation trend estimation with system resilience for rolling bearings

et al. 2023

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“…The data were centralized on different time intervals delimited to every ten faults that appeared (table 1). R0 represents the resilience scores over the time interval bounded by defects [1][2][3][4][5][6][7][8][9][10], R1 represents the resilience scores over the time interval determined by defects [11][12][13][14][15][16][17][18][19][20] and so on. The graphs of the values of the resilience function as a function of time for every ten defects that appeared in the system after the stabilization period of the technical system operation are presented in Figure 1.…”

Section: The Values Of Reliability Parameters [Mtbf] Maintainability ...mentioning

confidence: 99%

“…From a statistical point of view, the resilience of a system is correlated with the maintenance functions and the allocation of human and technical resources in order to ensure its operational continuity function [10,11]. In the specialized literature, there are correlations between the resilience levels of a technical system with the different maintenance strategies applied to it [12], but mathematical modelling of the maintenance strategy-resiliencesystemic topology type is impossible to achieve due to the increased variability of the mathematical model. The metrics used to calculate the resilience characteristic belong to complementary and very diversified fields: probabilistic [12], mathematical [13], Bayesian networks [14], statistical techniques [15], Monte Carlo simulation [5], fuzzy systems -relationships modelled by linguistic expressions and using fuzzy set theory [16].…”

Section: Introductionmentioning

confidence: 99%

“…In the specialized literature, there are correlations between the resilience levels of a technical system with the different maintenance strategies applied to it [12], but mathematical modelling of the maintenance strategy-resiliencesystemic topology type is impossible to achieve due to the increased variability of the mathematical model. The metrics used to calculate the resilience characteristic belong to complementary and very diversified fields: probabilistic [12], mathematical [13], Bayesian networks [14], statistical techniques [15], Monte Carlo simulation [5], fuzzy systems -relationships modelled by linguistic expressions and using fuzzy set theory [16]. Such a theory based on fuzzy mathematics constitutes support for the subjective or natural descriptors of the system characteristics and offers a methodology as natural as possible to allow the modelling of resilience from the design process of technical systems, [17].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Technical Resilience and Its Estimation by the Least Squares Method

Vîlcu,

Herghiligiu,

Verzea

et al. 2023

IJMMT

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The resilience of technical systems is an internal characteristic whose values of reliability, maintainability and availability parameters must be estimated in the design process. The difficulty of this approach derives from the fact that the resilience parameters are influenced by the behaviour of the technical system during its operation. For this reason, an analysis of the technical resilience of a technical system in operation is required, the analysis that the present research also makes. The current work aims to provide a tool for analyzing the evolution of technical resilience parameters - reliability, maintainability and availability during the operation of technical systems, useful in the decision-making process of maintenance management. The main purpose of the work is to determine the evolution of the score of the resilience parameter of a technical system in the mechanical field during operation. The parameter, taken from the specialized literature, used for the calculation of resilience is based on the Safety in operation indicators - mean time between failures (MTBF) for system reliability and mean time to repair (MTTR) for maintainability. These two parameters were calculated from the data obtained after the operation of a honing machine, including information about the moments of appearance of the defects, the repair time and the type of defect. The resilience evolution is given by a regression line generated by the least squares method on the measured and calculated data series. The regression analysis proposed in this research follows the evolution of the values of the technical resilience parameter over the time horizon over which the technical system was observed in order to the maintenance management decision process for the analyzed system in accordance with the quality and production policies. The research methodology is specific to a quantitative analysis applied to a technical process, continuing the formulation of the problem, the literature study on the researched topic, the determination and analysis of the series of values for the evaluated parameters, and the generation of conclusions on the analyzed phenomenon. This research's results are compared with those from the specialized literature that evaluates technical resilience through metrics on different mathematical models. Also, the research results are essential due to the comparative analysis of resilience in systems from other technical fields.

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“…is particularly prominent. In 1957, the Electronic Equipment Reliability Advisory Group of the U.S. Department of Defense gave the definition, indices and evaluation methods of reliability in the report "Reliability of Military Electronic Equipment", which laid the foundation for the development of reliability theory in the fields of complex equipment [2], machinery [3], software [4], manufacturing [5], design [6] and PLC management [7], and it provided support for the development of product DMS integration in the fields of aerospace equipment, new energy vehicles, high-end machine tool equipment and engines, and produced great economic benefits.…”

Section: Introductionmentioning

confidence: 99%

Construction of an Integrated Framework for Complex Product Design Manufacturing and Service Based on Reliability Data

et al. 2022

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With the application of new-generation information technology in the full life cycle process of a complex product, it is showing the characteristics of multi-source, real-time, heterogeneous, cross-domain transmission. Large data volume and low value density emerge in the process of complex product design manufacturing and services (DMS). This leads to “information islands” and insufficient utilization of cross-domain reliability data in the process of integration of DMS for complex product R&D design data, manufacturing data and operation and maintenance services (O&MS) data. This paper proposes and illustrates a framework of complex product DMS integration based on reliability data, including complex product design optimization based on manufacturing and service reliability data, complex product intelligent manufacturing process optimization based on real-time reliability data and complex product O&MS optimization based on multi-source heterogeneous reliability data. Additionally, it then realizes complex product design reliability and optimization, manufacturing process reliability and optimization and O&MS reliability and intelligent decision optimization based on reliability data. Finally, the DMS integration framework based on reliability-data-driven proposal is corrected through the case of engine MDS integration, which can effectively improve the cross-domain reliability data utilization and overall product reliability of complex products. The proposed framework extends the application of reliability theory in the process of complex product DMS integration and provides a reference for enterprises in the R&D, manufacturing and O&MS of complex products.

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Resilience Metric of Equipment System: Theory, Measurement and Sensitivity Analysis

Cited by 15 publications

References 49 publications

A different method of fault feature extraction under noise disturbance and degradation trend estimation with system resilience for rolling bearings

A different method of fault feature extraction under noise disturbance and degradation trend estimation with system resilience for rolling bearings

Evaluation of Technical Resilience and Its Estimation by the Least Squares Method

Construction of an Integrated Framework for Complex Product Design Manufacturing and Service Based on Reliability Data

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