Reliability Evaluation of Smart Meters Under Degradation-Shock Loads Based on Phase-Type Distributions

Dong, Xu; Xiao, Xiaoqi; Yu, Haibo

doi:10.1109/access.2020.2976200

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…Gong et al 18 constructed a generalized cumulative shock model to study a system subjected to shocks from various sources with different probabilities. Xu et al 19 investigated the reliability of the smart meters under degradation and cumulative shocks. Shamstabar et al 20 introduced a closed-form solution for reliability monitoring of a system subjected to cumulative shock failure based on PH distributions.…”

Section: Introductionmentioning

confidence: 99%

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Reliability modeling of systems with random failure threshold subjected to cumulative shocks using machine learning method

Shamstabar,

Ebrahimmagham,

Samimi

et al. 2024

Quality & Reliability Eng

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Modeling and evaluation of a system reliability is a key issue in shock model analysis. In cumulative shock model, a system fails when the total damages caused by shocks exceeds the system failure threshold level. The reliability of a system with random failure threshold level under cumulative shocks is investigated in this work, considering the general dependency between inter‐arrival times of shocks and their damages magnitudes. Clustering, as one of the unsupervised machine learning methods, is utilized here by applying a genetic algorithm (GA) and particle swarm optimization algorithm (PSO) for its implementation. Phase‐type (PH) distributions and their properties are also applied for finding a system failure distribution functions, determining the convolutions, evaluating the integrals, and ultimately modeling the system reliability. An illustrative example is provided to demonstrate the implementation and effectiveness of the proposed modeling procedure and the suggested approach for computing the system reliability and remaining useful lifetime (RUL). Also, an example case study of X‐ray machine in healthcare industry is provided to demonstrate the application of the proposed model. Applying other machine learning methods for patterns recognition is an area for future research.

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Section: Introductionmentioning

confidence: 99%

“…constructed a generalized cumulative shock model to study a system subjected to shocks from various sources with different probabilities. Xu et al 19 . investigated the reliability of the smart meters under degradation and cumulative shocks.…”

Section: Introductionmentioning

confidence: 99%

Reliability modeling of systems with random failure threshold subjected to cumulative shocks using machine learning method

Shamstabar,

Ebrahimmagham,

Samimi

et al. 2024

Quality & Reliability Eng

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show abstract

“…The safety and continuity of the power supply for thousands of households are dependent on the operational reliability of small meters. For the power supplier, reliability estimation is a significant technique to examine the quality of smart meters manufactured for power suppliers (Sharma and Saini, 2015;Xu et al, 2020). The power grid has a large number of smart meters, and it is impossible to test the reliability of all the smart meters in the power grid (Zhang et al, 2013;Depuru et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

A Study on Reliability of Smart Meters based on Monte-Carlo Method and Fault Trees

Chen¹,

Chen

Liao³

et al. 2021

DGAEJ

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Smart meters are widely used in the power supply system, and their operational reliability is closely related to the user’s power supply reliability. It is difficult for intelligent power metering equipment to accurately predict its operational reliability and lifespan based on the existing technical specifications. In order to improve the accuracy of predicting the reliability and the maintenance cycle of the smart meter, this paper proposes a method for predicting the reliability of the smart meter based on the Monte Carlo method and fault tree. Firstly, the occurrence time of the bottom sampling event is simulated by the Monte-Carlo method based on the statistical data of the annual failure rate of each module of the smart meter. Then, according to the Fault Tree analysis of smart meters, the occurrence of the event is transformed into the fault time of the whole smart meters. The interval statistics are used to obtain the reliability value of the smart meter. In the end, the curve of the reliability function is obtained after fitting the reliability value. The results show that the reliability of the smart meter obeys the exponential distribution during the operation of 100 years. When it comes to the tenth year, the reliability is 0.9519. This algorithm provides a guide for accurately predicting its reliability and maintenance cycles by modularly analyzing the faults of smart meters.

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Environmental Impact Assessment

2024

Urban Transportation Systems

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Reliability Evaluation of Smart Meters Under Degradation-Shock Loads Based on Phase-Type Distributions

Cited by 8 publications

References 27 publications

Reliability modeling of systems with random failure threshold subjected to cumulative shocks using machine learning method

Reliability modeling of systems with random failure threshold subjected to cumulative shocks using machine learning method

A Study on Reliability of Smart Meters based on Monte-Carlo Method and Fault Trees

Environmental Impact Assessment

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