An adaptive remaining useful life prediction approach for single battery with unlabeled small sample data and parameter uncertainty

Zhang, Jiusi; Jiang, Yuchen; Li, Xiang; Huo, Mingyi; Luo, Hao; Yin, Shen

doi:10.1016/j.ress.2022.108357

Cited by 104 publications

(19 citation statements)

References 42 publications

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“…In addition to the above, wide categories of simulation applications are relying on and in turn driving the development of AI. The typical application scenarios range from manufacturing to education, from smart city to entertainment, and many more beyond [119][120][121]. The enabling technologies include but are not limited to Internet of things, extended reality (XR), digital twin, robots, smart grids, space technologies, and so forth.…”

Section: Driving Forces Of Ai Developmentmentioning

confidence: 99%

Quo vadis artificial intelligence?

Jiang

Luo

et al. 2022

Discov Artif Intell

Self Cite

274

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The study of artificial intelligence (AI) has been a continuous endeavor of scientists and engineers for over 65 years. The simple contention is that human-created machines can do more than just labor-intensive work; they can develop human-like intelligence. Being aware or not, AI has penetrated into our daily lives, playing novel roles in industry, healthcare, transportation, education, and many more areas that are close to the general public. AI is believed to be one of the major drives to change socio-economical lives. In another aspect, AI contributes to the advancement of state-of-the-art technologies in many fields of study, as helpful tools for groundbreaking research. However, the prosperity of AI as we witness today was not established smoothly. During the past decades, AI has struggled through historical stages with several winters. Therefore, at this juncture, to enlighten future development, it is time to discuss the past, present, and have an outlook on AI. In this article, we will discuss from a historical perspective how challenges were faced on the path of revolution of both the AI tools and the AI systems. Especially, in addition to the technical development of AI in the short to mid-term, thoughts and insights are also presented regarding the symbiotic relationship of AI and humans in the long run.

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Section: Driving Forces Of Ai Developmentmentioning

confidence: 99%

Quo vadis artificial intelligence?

Jiang

Luo

et al. 2022

Discov Artif Intell

Self Cite

274

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“…Machine learning and deep learning 2 , 3 as typical data-driven methods for PHM have been attracting growing attention from academia and industry. CNN is the most widely used among various deep learning networks due to its powerful ability in feature extraction and complex representation learning, with many satisfying results achieved in bearing fault classification 4 – 8 and in RUL prediction 9 – 13 .…”

Section: Introductionmentioning

confidence: 99%

Light convolutional neural network by neural architecture search and model pruning for bearing fault diagnosis and remaining useful life prediction

Ruan

Han

Yan

et al. 2023

Sci Rep

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Convolutional Neural Network (CNN) has been extensively used in bearing fault diagnosis and Remaining Useful Life (RUL) prediction. However, accompanied by CNN’s increasing performance is a deeper network structure and growing parameter size. This prevents it from being deployed in industrial applications with limited computation resources. To this end, this paper proposed a two-step method to build a cell-based light CNN by Neural Architecture Search (NAS) and weights-ranking-based model pruning. In the first step, a cell-based CNN was constructed with searched optimal cells and the number of stacking cells was limited to reduce the network size after influence analysis. To search for the optimal cells, a base CNN model with stacking cells was initially built, and Differentiable Architecture Search was adopted after continuous relaxation. In the second step, the connections in the built cell-based CNN were further reduced by weights-ranking-based pruning. Experiment data from the Case Western Reserve University was used for validation under the task of fault classification. Results showed that the CNN with only two cells achieved a test accuracy of 99.969% and kept at 99.968% even if 50% connections were removed. Furthermore, compared with base CNN, the parameter size of the 2-cells CNN was reduced from 9.677MB to 0.197MB. Finally, after minor revision, the network structure was adapted to achieve bearing RUL prediction and validated with the PRONOSTIA test data. Both tasks confirmed the feasibility and superiority of constructing a light cell-based CNN with NAS and pruning, which laid the potential to realize a light CNN in embedded systems.

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“…The stochastic process with long-range dependence is also a 1/f noise in the frequency domain [ 26 ]. Combined with the Karman filter and the expectation maximation algorithm, the strong Markovian characteristics of Brownian motion can be improved [ 27 ]. To track the dynamics and multi-source variability of a degradation process together, a general time-varying Wiener process (GTWP) is proposed in [ 28 ].…”

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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An adaptive remaining useful life prediction approach for single battery with unlabeled small sample data and parameter uncertainty

Cited by 104 publications

References 42 publications

Quo vadis artificial intelligence?

Quo vadis artificial intelligence?

Light convolutional neural network by neural architecture search and model pruning for bearing fault diagnosis and remaining useful life prediction

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

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