A Prediction Method of Equipment Failure Rate Using Weibull Distribution and Arma

Abstract

The invention belongs to the technical field of equipment failure rate prediction of power systems, and in particular relates to a device failure rate prediction method using Weibull distribution combined with ARMA, which arranges the acquired equipment failure rate data in the order of time series to obtain the equipment failure rate time series data. According to the development trend of equipment failure rate, the failure period of the equipment is judged. Based on the judgment result of the failure period, Weibull distribution functions of different stages are selected to model the failure rate data, and the trend item of the failure rate time series data is determined. The time-series data of failure rate and its trend item are compared to obtain the time-series data of the random item of failure rate, and a time-series ARMA model is established for the random item based on the method of time series analysis. The prediction result of the trend item and the prediction result of the random item are added to obtain the prediction result of the equipment failure rate. The method combines the Weibull distribution function with the time series analysis model to achieve accurate prediction of equipment failure rates.

Description

technical field [0001] The invention belongs to the technical field of equipment failure rate prediction in electric power systems, and in particular relates to an equipment failure rate prediction method combining Weibull distribution and ARMA. Background technique [0002] The failure rate of electrical equipment is a set of random sequences formed over time, which is generally obtained by recording the actual operating conditions of the equipment for a long time and performing statistical analysis on the obtained data. On the one hand, due to the complexity of the geographical distribution of power equipment and the random diversity of operating states, the failure rate of power equipment has the characteristics of time-varying and random, and on the other hand, it has its own changing rules. Equipment failure rate is the basis of equipment reliability evaluation, so research on equipment failure rate prediction methods has an important impact on improving the reliability...

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Problems solved by technology

[0004] (1) There are many factors affecting the equipment failure rate and the influencing principles are complex, so it is difficult to establish a prediction model for the equipment failure rate and all influencing factors. The impact of equipment failure rate, which makes prediction more difficult

[0005] (2) The collection of equipment failure rate data is not strictly at equal time intervals, so it is used as data at equal time intervals for modeling analysis, ignoring the impact of sampling intervals on equipment failure rates, resulting in poor prediction accuracy

[0006] (3) Due to the limitation of statistical data on the reliability of power equipment, the number of available statistical samples is small, and it is difficult to find a suitable fitting function for modeling and fitting, resulting in poor fitting accuracy of equipment failure rate data

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