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Method for predicting equipment residual life based on improved particle filter algorithm

A particle filter algorithm and life prediction technology, applied in prediction, calculation, instrument, etc., can solve the problems of particle degradation, sample exhaustion, and low accuracy of remaining life prediction of equipment, and achieve accurate early prediction, high prediction accuracy, and suppression of particle degradation. Effect

Inactive Publication Date: 2017-06-13
SICHUAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

This patented technology helps improve how well an ensemble average (SCA) filters work with data accurately during regression analysis. It also includes adding extra steps after filtering out particles that may degrade certain attributes or affect their effectiveness on predicting future values based upon these properties. By incorporating this technique into the importance testing process, we aimed at reducing particle loss while maintaining high predicted precision for better understanding of complex systems like biological processes.

Problems solved by technology

The technical problem addressed in this patents relates to accurately estimating the lifespan of electronic devices like engines and vehicles while also minimizing wastage caused through maintenance operations. Current models rely heavily on particle filtering techniques alone without considering both aspects - particulate degrades and samples degrade.

Method used

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  • Method for predicting equipment residual life based on improved particle filter algorithm
  • Method for predicting equipment residual life based on improved particle filter algorithm
  • Method for predicting equipment residual life based on improved particle filter algorithm

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Embodiment

[0110] The specific implementation manner of the present invention will be described below in conjunction with a specific example—state estimation and life prediction of a lithium-ion battery system. Due to the complex working principle of lithium-ion batteries, it is difficult to measure its internal state parameters. Generally, the remaining life is predicted by collecting data such as current and voltage during the charging and discharging process of the battery, and establishing a mathematical model between capacity and cycle times. The specific process is as follows:

[0111] (1) Establish a general forecasting model

[0112]The target prediction model is established, and the system state update equation and measurement equation of the prediction object are shown in equations (19) and (20) respectively.

[0113] (2) Sensitive feature selection

[0114] Like most secondary batteries, the capacity of lithium-ion batteries will become smaller and smaller as the number of c...

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Abstract

The invention relates to the field of failure prediction of electromechanical equipment, discloses a method for predicting remaining life of equipment based on an improved particle filter algorithm, and improves the precision of life prediction of equipment. The improved particle filter algorithm used in the present invention includes an importance sampling stage and a resampling stage. In the importance sampling stage, the unscented Kalman filter method is introduced to update particles, and the proposed distribution is generated, thereby suppressing particle degradation; An off-chain Monte Carlo step that suppresses sample depletion. The invention is applicable to lithium batteries, rolling bearings and gear boxes.

Description

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Claims

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Application Information

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Owner SICHUAN UNIV
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