Research on optimization of control parameters of gravity shaking table

Due to the complex changes in physicochemical properties of lithium-ion batteries during the process from degradation to failure, it is difficult for methods based on physical or data-driven models to fully characterize this nonlinear process, and existing methods that hybridize physical and data-driven models suffer from ambiguous hybridization, which results in the vast majority of existing methods for predicting the RUL of lithium-ion batteries suffering from a lack of accuracy and robustness. In this study, a novel hybrid approach based on empirical modeling and data-driven techniques is proposed for predicting the remaining useful life (RUL) of lithium-ion batteries. To better capture its complexity, stochasticity, and state transition, and improve the modeling accuracy and RUL prediction precision, Gamma stochasticity and state-space modeling are used to empirically model the complex Li-ion battery degradation process. Moreover, the expectation maximization (EM) method of particle filtering (PF) was used to estimate the hidden parameters of the empirical model, and the estimated parameters were corrected using an optimized support vector regression (SVR) method to enhance the generalization performance and robustness of the data-driven model. The results show that the gamma state-space model is effective in capturing the inherent stochastic properties of the battery degradation and the proposed hybrid method outperforms the existing prediction methods in RUL prediction. The experiments show that the Sparrow Search Algorithm (SSA) optimized SVR is considered to be the most effective correction method for the estimated parameters, while the new EM-PF-SSA-SVR hybrid method provides better performance for state assessment and RUL prediction of lithium-ion batteries. It is indicated that the proposed EM-PF-SSA-SVR method with Gamma stochastic process has hybrid validity and superior performance with equal performance and less parameter computation relative to the existing state-of-the-art deep learning RUL prediction methods.

Section: Optimized Svr Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Remaining useful life prediction of lithium-ion batteries using EM-PF-SSA-SVR with gamma stochastic process

Keshun,

Guangqi,

Yingkui

2023

“…Zhang et al [51] proposed to use the moth flame optimization algorithm to adaptively select the parameters of the diagnostic model. You and Liu [52] proposed an adaptive optimization method for shaking table control parameters based on maximization of beneficiation efficiency through a data-driven sparrow search algorithm optimized support vector regression model. This optimization method is also used to estimate the parameter corrections, so that the model achieves the best predictive performance [53].…”

Section: Setting Of the Regularization Parametersmentioning

confidence: 99%

A fusion non-convex group sparsity difference method and its application in rolling bearing fault diagnosis

Wei,

Cai,

Liu

et al. 2023

Bearing fault is a common factor leading to machine failures. How to extract the periodic transient signal due to bearing faults submerged in strong noise is a challenging problem for bearing fault diagnosis. Total variation denoising is a method used to remove noise and extract features. However, it solely relies on the sparsity of the first-order difference of the signal, resulting in the loss of important features and underestimation of amplitude. Additionally, it fails to capture the periodicity of the signal. To address these issues, a novel approach called fusion non-convex group sparsity difference (FNC-GSD) method is proposed for bearing fault diagnosis. Firstly, it is recognized that noise does not exhibit sparsity in the difference domain, while transient signal exhibits group sparsity in the difference domain. This grouping property enhances the feature selection ability of sparse model. Inspired by this, the group sparsity of the transient signal in the difference domain is used to preserve the fault features as much as possible. Additionally, in order to promote sparsity of the signal itself in the time domain to preserve the potential impulse component, a l 1-norm regularization term is introduced. Furthermore, a non-convex sparsity-inducing penalty function strategy is employed to prevent amplitude underestimation. The proposed sparse model considers both the group sparsity in the difference domain and the sparsity in the time domain of the transient signal, and its solution is derived according to the majorization–minimization algorithm. And the particle swarm optimization algorithm is used to adaptively search the regularization parameters of FNC-GSD. Finally, multiple bearing fault diagnosis experiments are conducted to demonstrate the performance of the FNC-GSD. The results show that it has advantages in fault feature extraction compared with some other methods.

“…However, while many researchers focus on the precision of fault diagnosis, they often overlook its practical applications. Despite the promising accuracy results achieved by DL neural networks in the fault diagnosis of rolling bearings, the vast majority of these models are too complex with a large number of computational parameters, compromising their efficiency and hindering their practical deployment [32,33].…”

Section: Introductionmentioning

confidence: 99%

An efficient lightweight neural network using BiLSTM-SCN-CBAM with PCA-ICEEMDAN for diagnosing rolling bearing faults

You,

Qiu,

2023

Self Cite

This study proposes an efficient rolling bearing fault diagnosis model of a hybrid neural network with a lightweight attention mechanism. Firstly, to achieve the low complexity of deep learning computation, data reduction and denoising are performed by sparse convolutional network (Principal Component Analysis and Improved Complete Ensemble Empirical Modal Decomposition of Adaptive Noise), then processed data is imported to the hybrid neural network model with CBAM (Convolutional Block Attention Module ). The BiLSTM-SCN (Bi-directional Long Short-Term Memory and Sparse Convolutional Networks ) are used in the backbone of the model. A lightweight, generalized attention mechanism is introduced to the last layer of the model for enhancing feature learning, which can further improve the diagnostic accuracy and efficiency. Compared with existing deep learning fault diagnosis models, In simulating the most realistic cross-conditions and cross-platform conditions, which leads to the random nature of fault generation and makes model diagnosis more complex, the proposed method still maintains less running time and excellent diagnostic accuracy. Finally, the experimental results fully prove that the model has reliable robust and efficient, and it achieves the best balance of diagnostic accuracy and diagnostic efficiency of the hybrid deep learning model.