Results 41 to 50 of about 10,541,846 (196)

Real-Time Remaining Useful Life Prediction of Cutting Tools Using Sparse Augmented Lagrangian Analysis and Gaussian Process Regression

Sensors, 2022
Remaining useful life (RUL) of cutting tools is concerned with cutting tool operational status prediction and damage prognosis. Most RUL prediction methods utilized different features collected from different sensors to predict the life of the tool.
Xiao Qin, Weizhi Huang, Xuefei Wang, Zezhi Tang, Zepeng Liu   +4 more
doaj   +1 more source

Intelligent Maintenance Review for Robots: Multimodal Information, Deep Diagnosis and Embodied Artificial Intelligence

Advanced Robotics Research, EarlyView.
This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao, Hongbo Wang, Naipeng Li, Shuo Wang, Yaguo Lei, Tonghai Wu, Junyi Cao   +6 more
wiley   +1 more source

Multiscale similarity ensemble framework for remaining useful life prediction

, 2022
Art. 110565Accurate prediction of remaining useful life (RUL) is crucially important to perform prognostics and health management. A new similarity-based autoencoder multiscale ensemble (Similarity-based AE MSEN) methodology is proposed in this paper to ...
Xia, T., Wang, D., Xu, Y., Shu, J., Zheng, Y.   +4 more
core   +1 more source

Predictive Maintenance of Manufacturing Industry Machines Using Remaining Useful Life (RUL) Prediction

International Journal of Computer Science and Mobile Computing
In this paper, a lightweight Autoencoder-LSTM is provided to predict Remaining Useful Life (RUL) of bearings in real-life circumstances with noisy sensors and a small amount of labeled information. The method involves unsupervised health indicator (HI) construction and time modelling that is supervised.
Vishwa Kiran K H, Mohan S H, Rohit M N, Divyashri C R   +3 more
openaire   +1 more source

Correction: Prediction of remaining useful life (RUL) of Komatsu excavator under reliability analysis in the Weibull-frailty model

PLOS ONE, 2021
[This corrects the article DOI: 10.1371/journal.pone.0236128.].
Awat Ghomghaleh, Reza Khaloukakaie, Mohammad Ataei, Abbas Barabadi, Ali Nouri Qarahasanlou, Omeid Rahmani, Amin Beiranvand Pour   +6 more
openaire   +3 more sources

Remaining useful life prognosis of turbofan engines based on deep feature extraction and fusion

Scientific Reports, 2022
In turbofan engine datasets, to address problems, such as noise interference, diverse data types, large data volumes, complex feature extraction, inability to effectively describe degradation trends, and poor remaining useful life (RUL) prognosis effects,
Cheng Peng, Yufeng Chen, Weihua Gui, Zhaohui Tang, Changyun Li   +4 more
doaj   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

A Probabilistic Fractional Order Physics Informed Mamba Kolmogorov‐Arnold Network for State‐of‐Charge Estimation in Grid‐Connected Battery Energy Storage Systems

Energy Science &Engineering, EarlyView.
This paper introduces the probabilistic fractional‐order Mam‐KAN (PFO‐Mam‐KAN) controller, a physics‐informed gray‐box framework for real‐time battery state‐of‐charge estimation. By unifying efficient Mamba encoders with uncertainty‐aware fractional physics, it achieves superior 0.31% RMSE accuracy and robust grid‐support operation under dynamic ...
Arun Kumar Rawat, Subhash Chandra, Vinay Kumar Deolia   +2 more
wiley   +1 more source

A New Model for Remaining Useful Life Prediction Based on NICE and TCN-BiLSTM under Missing Data

Machines, 2022
The Remaining Useful Life (RUL) prediction of engineering equipment is bound to face the situation of missing data. The existing methods of RUL prediction for such cases mainly take “data generation—RUL prediction” as the basic idea but are often limited
Jianfei Zheng, Bowei Zhang, Jing Ma, Qingchao Zhang, Lihao Yang   +4 more
doaj   +1 more source

Prediction of remaining useful life (RUL) of Komatsu excavator under reliability analysis in the Weibull-frailty model

PLOS ONE, 2020
Es una tarea esencial estimar la vida útil restante (RUL) de la maquinaria en el sector minero destinada a garantizar la producción y la satisfacción del cliente. En este estudio, se utilizó un marco conceptual para determinar el RUL bajo el análisis de confiabilidad en un modelo de fragilidad. El marco propuesto se implementó en una excavadora Komatsu
Awat Ghomghaleh, Reza Khaloukakaie, Mohammad Ataei, Abbas Barabadi, Ali Nouri Qarahasanlou, Omeid Rahmani, Amin Beiranvand Pour   +6 more
openaire   +4 more sources