A reliability evaluation framework for smart meters based on AGG-ARIMA and PFR

Ma, Lisha; Meng, Zhiqiang; Teng, Zhaosheng; Qiu, Wei

doi:10.1088/1361-6501/ac42e6

Cited by 9 publications

(3 citation statements)

References 34 publications

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“…Since the leakage monitoring of NPPs generally involves data collection at fixed time intervals, the time-series model can be used to model the leakage degradation process of NPPs. In this paper, we introduce the most commonly used autoregressive integrated moving average (ARIMA) model [ 47 , 48 ].…”

Section: Structure Of the Integrated Methodsmentioning

confidence: 99%

An integrated method for the leakage fault mode diagnosis and life prediction of the reactor coolant pump

Shao,

Kang,

et al. 2024

PLoS ONE

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The reactor coolant pump is a key equipment in a nuclear power plant. If the leakage exceeds a certain threshold, it may cause reactor overheating and shutdown. The reactor coolant pump leakage fault usually has two problems: corrosion and scaling. Accurately and efficiently diagnosing the leakage fault mode as early as possible and predicting its remaining useful life (RUL) are important for taking timely maintenance measures. In this paper, an integrated method is proposed. First, the cross-sectional area of the first seal is extracted as a fault indicator. The motivation is that corrosion may enlarge the cross-sectional area, and scaling may reduce the cross-sectional area. Based on the fluid mechanics theory, an integrated model with several uncertain parameters is established among the cross-sectional area, temperature, and leakage at the inlet and outlet of the first seal. In the diagnosing process, a modified change-detection method is proposed to detect the starting point of degradation. Then, the unknown parameters in the previous relation are estimated, and the degrading data before the starting point of degradation are used to diagnose the leakage fault mode. Second, a time-series model of the autoregressive integrated moving average (ARIMA) is established to predict the remaining useful life based on the degrading data after the starting point of degradation. Finally, the leakage degrading data from six reactor coolant pumps of a nuclear power plant is used to perform the leakage fault mode diagnosis and life prediction with degradation point detection error rates not exceeding 4%, fault mode diagnosis correction rates 100% and practical RUL predicting results, which proves that the proposed integrated method is accurate and efficient. The proposed integrated method combines the advantages of both the physical model diagnosis and the data-driven model diagnosis and innovatively make use of the quantity of flow from the output side of the primary pump as the monitoring indicator and the cross-sectional area as the characteristic index together to diagnose the leakage fault mode happened to the seal and predict its RUL, which can meet the needs of actual operation and maintenance to ensure a healthy and stable operation of the pump and prevent unexpected shutdowns of nuclear power plants and serious accidents.

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Section: Structure Of the Integrated Methodsmentioning

confidence: 99%

An integrated method for the leakage fault mode diagnosis and life prediction of the reactor coolant pump

Shao,

Kang,

et al. 2024

PLoS ONE

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“…The geographical and climatic environments in which the power metering devices are located will negatively impact their metering effect (Ma et al, 2022). In practical environments, smart meters and other power metering equipment may encounter different product problems, such as high cold, high altitude, and high humidity and heat.…”

Section: Analysis Of Environmental Influences On the Performance Of P...mentioning

confidence: 99%

Examining performance calibration in smart power system electricity metering based on environmental perception attention LSTM-network

Zhang,

Xia,

et al. 2024

Front. Energy Res.

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The operating environment greatly influences the accuracy of power metering devices, resulting in variations and inconsistencies in measurement results across different working situations. A calibration model for power metering devices is proposed in this study, considering a range of environmental circumstances. The first step involves investigating the environmental conditions that impact the accuracy of power metering devices. The mutual information approach is utilized to identify environmental disturbances affecting device accuracy. A machine learning-driven symmetry attention Long Short-Term Memory (LSTM) network addresses measurement errors, capitalizing on the network’s symmetry data knowledge. Ultimately, the efficacy of the suggested approach is substantiated through the utilization of performance indicators, namely, Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results show that the proposed method can effectively reduce the errors of the power measurement device in all quarters, and the error reduction effect is over 10% in the spring, which is better than other models, demonstrating exemplary performance in correcting the calibration errors of the power measurement device.

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“…It is not suitable for products without data. Lisha [6] mentioned adaptive Gauss genetic algorithm-autoregressive integrated moving average method and combined with proportional FR to propose a reliability evaluation method which is more suitable for small samples to analyze the overall quality of meters from different suppliers. Yang [7] proposed a method to establish a comprehensive life model for the meter that can describe different stress ratios based on reliability physics and big data analysis by using a large number of abnormal data and maintenance data generated by the meter during field operation.…”

Section: Introductionmentioning

confidence: 99%

Research on the Life Prediction Method of Meters Based on a Nonlinear Wiener Process

et al. 2022

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Due to the high reliability of present meters, it is difficult to obtain the failure time of meters through accelerated life tests. Based on the failure data of the accelerated life test, this paper studies the mathematical model based on the Wiener process and establishes the degradation model of the instrument by the maximum likelihood to estimate the parameters of the Wiener model. With full consideration of the possible nonlinear effects in modeling, the time scale transformation method is used to study and obtain the reliability life prediction model of smart meters based on nonlinear data. Finally, the reliability life prediction model of meters is verified and evaluated through the example data of the accelerated life test of smart meters. Compared with the conventional method, this method has less error in calculating the reliability, greatly saves test time, and has a higher accuracy than estimating the lifetime model using the Wiener process directly.

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A reliability evaluation framework for smart meters based on AGG-ARIMA and PFR

Cited by 9 publications

References 34 publications

An integrated method for the leakage fault mode diagnosis and life prediction of the reactor coolant pump

An integrated method for the leakage fault mode diagnosis and life prediction of the reactor coolant pump

Examining performance calibration in smart power system electricity metering based on environmental perception attention LSTM-network

Research on the Life Prediction Method of Meters Based on a Nonlinear Wiener Process

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