A review on physics-informed data-driven remaining useful life prediction: Challenges and opportunities

Li, Huiqin; Zhang, Zhengxin; Li, Tianmei; Si, Xiaosheng

doi:10.1016/j.ymssp.2024.111120

Cited by 15 publications

(3 citation statements)

References 176 publications

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“…The one-dimensional convolutional layer consists of N parallel Wdimensional convolutional kernels, where N and W are hyperparameters. Firstly, the number of types of input features is F, and the length of input segments is L. We fill (w − 1)/2 zero vectors before and after the input matrix M input to obtain M padding , and the sequence is shown in Equation (10) as follows:…”

Section: Output Layermentioning

confidence: 99%

“…This method relies on collected battery aging data to extract and analyze information related to battery capacity degradation, enabling the estimation of the SOH for LIBs [8,9]. Since pure data-driven methods for capacity estimation lack transparency and interpretability [10], embedding physical or domain knowledge into the development of machine learning models has received more attention [11,12]. The use of a physics-informed neural network (PINN) is a classic method in this type of solution.…”

Section: Introductionmentioning

confidence: 99%

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Method for Evaluating Degradation of Battery Capacity Based on Partial Charging Segments for Multi-Type Batteries

Sun,

Tian,

et al. 2024

Batteries

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Accurately estimating the capacity degradation of lithium-ion batteries (LIBs) is crucial for evaluating the status of battery health. However, existing data-driven battery state estimation methods suffer from fixed input structures, high dependence on data quality, and limitations in scenarios where only early charge–discharge cycle data are available. To address these challenges, we propose a capacity degradation estimation method that utilizes shorter charging segments for multiple battery types. A learning-based model called GateCNN-BiLSTM is developed. To improve the accuracy of the basic model in small-sample scenarios, we integrate a single-source domain feature transfer learning framework based on maximum mean difference (MMD) and a multi-source domain framework using the meta-learning MAML algorithm. We validate the proposed algorithm using various LIB cell and battery pack datasets. Comparing the results with other models, we find that the GateCNN-BiLSTM algorithm achieves the lowest root mean square error (RMSE) and mean absolute error (MAE) for cell charging capacity estimation, and can accurately estimate battery capacity degradation based on actual charging data from electric vehicles. Moreover, the proposed method exhibits low dependence on the size of the dataset, improving the accuracy of capacity degradation estimation for multi-type batteries with limited data.

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Section: Output Layermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Method for Evaluating Degradation of Battery Capacity Based on Partial Charging Segments for Multi-Type Batteries

Sun,

Tian,

et al. 2024

Batteries

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show abstract

“…Furthermore, the natural variability in tool wear in machining processes [ 25 ] leads to a situation in which different tools—even though they are used in identical cutting conditions—exhibit highly different wear evolution. Any model that produces a single value estimate places the practitioner in a difficult position when assessing how accurate the prediction is [ 26 ]. In practice, a confidence interval or distribution is required in order to allow an estimation of the reliability of the prediction [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Confidence Interval Estimation for Cutting Tool Wear Prediction in Turning Using Bootstrap-Based Artificial Neural Networks

Colantonio,

Equeter,

Dehombreux

et al. 2024

Sensors

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The degradation of the cutting tool and its optimal replacement is a major problem in machining given the variability in this degradation even under constant cutting conditions. Therefore, monitoring the degradation of cutting tools is an important part of the process in order to replace the tool at the optimal time and thus reduce operating costs. In this paper, a cutting tool degradation monitoring technique is proposed using bootstrap-based artificial neural networks. Different indicators from the turning operation are used as input to the approach: the RMS value of the cutting force and torque, the machining duration, and the total machined length. They are used by the approach to estimate the size of the flank wear (VB). Different neural networks are tested but the best results are achieved with an architecture containing two hidden layers: the first one containing six neurons with a Tanh activation function and the second one containing six neurons with an ReLu activation function. The novelty of the approach makes it possible, by using the bootstrap approach, to determine a confidence interval around the prediction. The results show that the networks are able to accurately track the degradation and detect the end of life of the cutting tools in a timely manner, but also that the confidence interval allows an estimate of the possible variation of the prediction to be made, thus helping in the decision for optimal tool replacement policies.

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Advances and limitations in machine learning approaches applied to remaining useful life predictions: a critical review

Qiao,

Jauw,

Seong

et al. 2024

Int J Adv Manuf Technol

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A review on physics-informed data-driven remaining useful life prediction: Challenges and opportunities

Cited by 15 publications

References 176 publications

Method for Evaluating Degradation of Battery Capacity Based on Partial Charging Segments for Multi-Type Batteries

Method for Evaluating Degradation of Battery Capacity Based on Partial Charging Segments for Multi-Type Batteries

Confidence Interval Estimation for Cutting Tool Wear Prediction in Turning Using Bootstrap-Based Artificial Neural Networks

Advances and limitations in machine learning approaches applied to remaining useful life predictions: a critical review

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