A variational local weighted deep sub-domain adaptation network for remaining useful life prediction facing cross-domain condition

Zhang, Jiusi; Li, Xiang; Tian, Jilun; Jiang, Yuchen; Luo, Hao; Yin, Shen

doi:10.1016/j.ress.2022.108986

Cited by 83 publications

(24 citation statements)

References 26 publications

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“…The CPES is made of 𝐿 components: the generic 𝑙-th component, 𝑙 ∈ Λ = {1, … , 𝐿}, is assumed to be equipped with PHM capabilities, which allow estimating its RUL. In [40][41][42], several advanced machine learning methods have been recently proposed to estimate the RUL, given the ground truth failure time 𝑇 𝑙 * of the 𝑙-th component, is equal to:…”

Section: Problem Formulationmentioning

confidence: 99%

A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply

Hao

Maio

Zio

2023

Reliability Engineering & System Safety

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Section: Problem Formulationmentioning

confidence: 99%

A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply

Hao

Maio

Zio

2023

Reliability Engineering & System Safety

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“…Jiusi Zhang et al [27] proposed a novel parallel hybrid model that is based on a 1D CNN and a bidirectional gated recurrent unit for the prediction of the remaining useful life and attained promising results using two datasets, i.e., the aircraft turbofan engine dataset and the milling dataset. In another study [28], the authors suggested a new method for predicting the remaining useful life, called VLSTM-LWSAN. They used data from turbofan engines to confirm the effectiveness of their suggested concept.…”

Section: Related Workmentioning

confidence: 99%

The Role of Socioeconomic Factors in Improving the Performance of Students Based on Intelligent Computational Approaches

et al. 2023

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Nowadays, we are living in the modern era of technological revolution and globalization, where people are giving more priority to proper education to compete among the top countries and to achieve something in their lives. Education improves a person’s abilities and creativity, which in turn have a positive effect on the development of a nation’s or an individual’s economy and also play a productive role in it. The traditional approaches are based only on statistical measures and are not capable of figuring out the most significant socioeconomic factors affecting the performance of a student. Keeping in mind the significance of socioeconomic status (SES) in improving the performance of a student, this study analyzes the important socioeconomic factors that affect the performance of a student in Khyber Pakhtunkhwa, Pakistan. We developed our own dataset by collecting data from 100 different schools (both government and private) in Khyber Pakhtunkhwa, Pakistan, consisting of more than 5550 students who were given a proper questionnaire survey. The created dataset consists of a total of 18 features and a target class. In this research, we used different statistical and machine learning (ML) methodologies to identify the most crucial elements that significantly affect the academic achievements of a student and have a strong correlation with the target class. To select the most prominent features from the dataset, we used two different feature selectors (FCBF and relief) and measured their performances along with ML models. To measure the significance rate of each ML algorithm using the full and selected feature space, we used different performance measures such as accuracy, precision, recall, sensitivity, specificity, etc. The experimental outcomes show that the feature selection algorithms significantly improve the performance of the classification models by providing more relevant features that have a strong association with the target class. This study also offered some advice for decision-makers, particularly in the respective education sector and other authorities, to develop specific solution strategies, plans, and initiatives to address the issue. It is envisioned that the suggested scheme will help the residents of Khyber Pakhtunkhwa province, in particular, obtain a high-quality education that can help pave the way for an educated and developed Pakistan.

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“…In engineering application, acquiring sufficient data may be difficult. For the cross-domain condition of the RUL prediction, a variational local weighted deep sub-domain adaptation network is proposed [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Fractal Weibull Adaptive Model for the Remaining Useful Life Prediction of Electric Vehicle Lithium Batteries

Deng

Gao

Chen

et al. 2023

Entropy

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In this paper, an adaptive remaining useful life prediction model is proposed for electric vehicle lithium batteries. Capacity degradation of the electric car lithium batteries is modeled by the multi-fractal Weibull motion. The varying degree of long-range dependence and the 1/f characteristics in the frequency domain are also analyzed. The age and state-dependent degradation model is derived, with the associated adaptive drift and diffusion coefficients. The adaptive mechanism considers the quantitative relations between the drift and diffusion coefficients. The unit-to-unit variability is considered a random variable. To facilitate the application, the convergence of the RUL prediction model is proved. Replacement of the lithium battery in the electric car is recommended according to the remaining useful life prediction results. The effectiveness of the proposed model is shown in the case study.

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A variational local weighted deep sub-domain adaptation network for remaining useful life prediction facing cross-domain condition

Cited by 83 publications

References 26 publications

A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply

A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply

The Role of Socioeconomic Factors in Improving the Performance of Students Based on Intelligent Computational Approaches

Multi-Fractal Weibull Adaptive Model for the Remaining Useful Life Prediction of Electric Vehicle Lithium Batteries

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