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Method of predicting remaining life of equipment based on improved unscented particle filtering

A particle filter and life prediction technology, which is applied in prediction, design optimization/simulation, special data processing applications, etc., can solve problems such as missing, and achieve the effect of reducing particle degradation

Active Publication Date: 2017-11-28
SICHUAN UNIV
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AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a method for predicting the remaining life of equipment based on improved odorless particle filtering, to solve the problem of lack of particle diversity in traditional equipment remaining life prediction methods for odorless particle filtering, and to improve the accuracy of equipment life prediction

Method used

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  • Method of predicting remaining life of equipment based on improved unscented particle filtering
  • Method of predicting remaining life of equipment based on improved unscented particle filtering
  • Method of predicting remaining life of equipment based on improved unscented particle filtering

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Embodiment

[0048] Embodiments Taking lithium-ion batteries as an example, a method for predicting the remaining life of equipment based on improved odorless particle filtering is provided, and the specific steps are as follows:

[0049] Step 1: Select training data, and perform curve fitting on the training data based on the degradation model.

[0050] This step uses the empirical degradation model, and uses the BatteryData Set test data provided by NASA's Fault Prediction Center of Excellence as training data. In this example, the data of batteries No. 5, 6 and 7 are used as training data, and the data of No. 18 batteries are used For life prediction, use the matlab toolbox to perform curve fitting on the data of No. 5, No. 6, No. 7, and No. 18 batteries. The obtained results are as follows figure 2 As shown, the empirical degradation model is:

[0051] Q=a·exp(b·k)+c·exp(d·k)

[0052] Q is the lithium battery capacity; a, b, c, and d are model parameters; k is the number of cycles. ...

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Abstract

The invention relates to the field of life prediction of mechanical and electrical equipment, and discloses a method of predicting the remaining life of equipment based on improved unscented particle filtering, in order to solve the problem that there is a loss of particle diversity for unscented particle filtering in the traditional method of predicting the remaining life of equipment, and to improve the accuracy of equipment life prediction. Unscented Kalman filtering is used as a recommended density distribution function of particle filtering to reduce particle degradation, and a linear optimization re-sampling algorithm is used to optimize the re-sampling part of particle filtering. For the selection of the step length coefficient K of linear optimization re-sampling, an adjustment factor Kb is created, the value of the step length coefficient K is determined adaptively using a fuzzy reasoning system, and finally, the remaining useful life of equipment is predicted. The method of the invention is suitable for residual useful life prediction of mechanical and electrical equipment.

Description

technical field [0001] The invention relates to the field of life prediction of electromechanical equipment, in particular to a method for predicting remaining life of equipment based on improved odorless particle filtering. Background technique [0002] With the rapid development of modern technology and industrial technology and the continuous improvement of functional requirements, the complexity, comprehensiveness and intelligence level of a large number of electromechanical equipment continue to increase. At the same time, the reliability and safe operation of equipment are becoming more and more important. Electromechanical equipment has inevitable performance degradation during operation. When the performance of the equipment degrades to the point that the equipment is not enough to complete its function, it will lead to equipment downtime or even failure, resulting in huge economic losses and even casualties. Accurately predicting the remaining useful life of equipm...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06F17/50
CPCG06Q10/04G06F30/20
Inventor 苗强张恒张新刘治汶
Owner SICHUAN UNIV
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