Equipment Remaining Life Prediction Method Based on Improved Odorless Particle Filter

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

Active Publication Date: 2021-02-05
SICHUAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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

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  • Equipment Remaining Life Prediction Method Based on Improved Odorless Particle Filter
  • Equipment Remaining Life Prediction Method Based on Improved Odorless Particle Filter
  • Equipment Remaining Life Prediction Method Based on Improved Odorless Particle Filter

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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 electromechanical equipment, and discloses a method for predicting the remaining life of equipment based on improved odorless particle filtering, which solves the problem of lack of particle diversity existing in the traditional method for predicting the remaining life of equipment by odorless particle filtering, and improves the accuracy of equipment life prediction. . The present invention reduces particle degradation by using tasteless Kalman filter as the proposed density distribution function of particle filter, and adopts linear optimization resampling algorithm to optimize the resampling part of particle filter; The selection of , the present invention creates an adjustment factor K by creating b , and use the fuzzy inference system to determine the value of the step size coefficient K adaptively, and finally realize the prediction of the remaining effective life of the equipment. The present invention is suitable for remaining useful life prediction of electromechanical 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 & AuthorityPatents(China)
IPC IPC(8): G06Q10/04G06F30/20
CPCG06Q10/04G06F30/20
Inventor苗强张恒张新刘治汶
OwnerSICHUAN UNIV