Article
Thermodynamics
Zhiqiang Lyu, Geng Wang, Renjing Gao
Summary: This study proposes a hybrid kernel function relevance vector machine (HKRVM) optimized model for battery prognostics and health management. It extracts ageing features (AFs) from the incremental capacity curve to monitor battery state of health (SOH) and predicts remaining useful life (RUL) using a metabolic extreme learning machine. The HKRVM captures the relationship between AFs and capacity and determines optimal weights and kernel parameters using a genetic grey wolf optimizer (GGWO).
Article
Chemistry, Multidisciplinary
Kaidi Gao, Jingyun Xu, Zuxin Li, Zhiduan Cai, Dongming Jiang, Aigang Zeng
Summary: A hybrid method is proposed in this paper to predict the remaining useful life (RUL) of lithium-ion batteries (LIBs) in order to be prepared for future capacity deterioration. The method utilizes an empirical degradation model, a particle filter algorithm, and a discrete wavelet transform algorithm to improve prediction performance and data validity. Experimental results demonstrate significant improvements in both long-short-term deterioration progress and RUL prediction tasks.
Article
Engineering, Electrical & Electronic
Long Chen, Jun Zhao, Wei Wang, Qingshan Xu
Summary: This study introduces an RVM prediction model with input noise to handle input uncertainty, utilizes a Gaussian approximation for input uncertainty, and employs the Markov chain Monte Carlo algorithm to approximate the posterior distribution over model weights, resulting in improved prediction performance.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Automation & Control Systems
Xiuli Wang, Bin Jiang, Steven X. Ding, Ningyun Lu, Yang Li
Summary: RUL prediction plays a significant role in component health management, and this study focuses on accurately predicting RUL under uncertain conditions. The study extends the relevance vector machine (RVM) model into the probability manifold to enhance prediction accuracy and develops a dynamic multistep regression model to account for the influence of uncertainties. RUL is predicted based on estimating the degradation tendency and using the first hitting time (FHT) method.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Engineering, Electrical & Electronic
Wei Guo, Mao He
Summary: This study proposes an integrated prognostics method for rolling element bearings to improve the accuracy of remaining useful life (RUL) prediction. A new health indicator is introduced and a relevance vector machine regression model is used to achieve higher accuracy even for long-term predictions.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Computer Science, Interdisciplinary Applications
Moncef Soualhi, Khanh T. P. Nguyen, Kamal Medjaher, Fatiha Nejjari, Vicenc Puig, Joaquim Blesa, Joseba Quevedo, Francesc Marlasca
Summary: Data-driven prognostics and health management is crucial for the future industry, allowing accurate estimation of system RUL through machine learning algorithms. However, the high variability in end-of-life time due to different fault types and degradation rates results in uncertainties in RUL estimation.
COMPUTERS IN INDUSTRY
(2023)
Article
Engineering, Civil
Yuan-Hao Wei, You-Wu Wang, Yi-Qin Ni
Summary: The study aims to develop a wheel defect detection approach based on RVM that can detect defects online using trackside monitoring data under different running-speed conditions. By extracting CFA from dynamic strain responses and formulating multiple probabilistic regression models (MPRMs) using multi-kernel RVM, the proposed approach demonstrates better local and global representation ability and generalization performance for reliable defect detection. The method is validated using real-world monitoring data acquired by an FBG-based trackside monitoring system, showing its effectiveness under different running-speed conditions.
SMART STRUCTURES AND SYSTEMS
(2022)
Article
Engineering, Aerospace
Raul Llasag Rosero, Catarina Silva, Bernardete Ribeiro
Summary: Predictive Maintenance (PM) strategies are of interest in the aviation industry to reduce maintenance costs and Aircraft On Ground (AOG) time. This paper proposes the integration of a physics-based model with a data-driven model to predict the Remaining Useful Life (RUL) of aircraft cooling units. The results show that the cooling units experience a normal degradation stage before an abnormal degradation that occurs within the last flight hours of useful life.
Article
Engineering, Electrical & Electronic
Bo Jiang, Haifeng Dai, Xuezhe Wei, Zhao Jiang
Summary: A reliable cycling aging prediction model based on data-driven methods is proposed in this study to address the adaptive and early prediction of lithium-ion battery remaining useful life. The model utilizes a multi-kernel RVM with particle swarm optimization to enhance learning and generalization abilities. A similarity criterion of battery capacity curves is proposed for early life prediction. Experimental results demonstrate the accurate prediction of failure cycle and capacity attenuation trajectory for different types of batteries, as well as the ability to learn general fading characteristics from other battery types.
IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS
(2023)
Article
Engineering, Multidisciplinary
Xieyi Chen, Yi Wang, Haoran Sun, Hulin Ruan, Yi Qin, Baoping Tang
Summary: Gear is crucial for mechanical equipment, and its health directly influences the overall operation of the equipment. Therefore, accurately predicting the remaining useful life (RUL) of gearboxes is of great significance. However, current deep learning-based RUL prediction methods often overlook trend characteristics and focus on the fluctuation patterns of degradation data. To address this issue, a generalized degradation tendency tracking strategy (GDTTS) is proposed to improve the prediction performance by capturing both trend and fluctuation characteristics. Experimental results on real gearbox datasets demonstrate the effectiveness of the proposed strategy.
Article
Automation & Control Systems
Leonardo Ramos Rodrigues, Takashi Yoneyama
Summary: This paper proposed a novel repair priority rule based on a Prognostics and Health Monitoring system, and numerical experiments showed that it consistently reduces inventory system cost.
Article
Automation & Control Systems
Zhenghua Chen, Min Wu, Rui Zhao, Feri Guretno, Ruqiang Yan, Xiaoli Li
Summary: This article proposes an attention-based deep learning framework for the prediction of machine's remaining useful life (RUL). By integrating handcrafted features with automatically learned features and developing a feature fusion framework, the performance of RUL prediction can be improved.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Engineering, Mechanical
Tao Yan, Yaguo Lei, Naipeng Li, Xiaosheng Si, Liliane Pintelon, Reginald Dewil
Summary: This paper proposes a method for ensemble RUL prediction that takes into account the nonlinear relationships among individual prediction models and formulates an online joint replacement-order model using the ensemble RUL prediction results. Experimental results show that the proposed method has higher accuracy and provides more effective joint policies.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Multidisciplinary
Ranran Wang, Hailin Feng
Summary: A novel health indicator (HI) is proposed to address the difficulties in obtaining traditional indicators for RUL prediction. Extracted from battery current profiles, optimized by Box-Cox transformation, and used in probabilistic prediction with RVM algorithm, the HI effectively improves the accuracy of LIB RUL prediction.
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL
(2021)
Article
Acoustics
Gang Zhang, Weige Liang, Bo She, Fuqing Tian
Summary: This study proposed a remaining useful life prediction method that combines deep-learning based health indicators and relevant vector machines, the effectiveness of which was demonstrated through experiments.
SHOCK AND VIBRATION
(2021)
Article
Engineering, Electrical & Electronic
Licheng Liu, C. L. Philip Chen, Xinge You, Yuan Yan Tang, Yushu Zhang, Shutao Li
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2018)
Article
Computer Science, Artificial Intelligence
Xiaojie Su, Fengqin Xia, Ligang Wu, C. L. Philip Chen
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2018)
Article
Automation & Control Systems
Guanyu Lai, Changyun Wen, Zhi Liu, Yun Zhang, C. L. Philip Chen, Shengli Xie
INTERNATIONAL JOURNAL OF CONTROL
(2018)
Article
Automation & Control Systems
Min Gan, C. L. Philip Chen, Guang-Yong Chen, Long Chen
IEEE TRANSACTIONS ON CYBERNETICS
(2018)
Article
Automation & Control Systems
Guanyu Lai, Changyun Wen, Zhi Liu, Yun Zhang, C. L. Philip Chen, Shengli Xie
Article
Automation & Control Systems
Yan-Jun Liu, Shumin Lu, Shaocheng Tong, Xinkai Chen, C. L. Philip Chen, Dong-Juan Li
Article
Computer Science, Artificial Intelligence
Shuang Feng, C. L. Philip Chen
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2018)
Article
Computer Science, Artificial Intelligence
Guanyu Lai, Zhi Liu, C. L. Philip Chen, Yun Zhang, Xin Chen
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2018)
Article
Computer Science, Artificial Intelligence
Zhizhong Han, Zhenbao Liu, Chi-Man Vong, Yu-Shen Liu, Shuhui Bu, Junwei Han, C. L. Philip Chen
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2018)
Article
Automation & Control Systems
Guoxing Wen, C. L. Philip Chen, Yan-Jun Liu
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2018)
Article
Automation & Control Systems
Tie Qiu, Kaiyu Zheng, Min Han, C. L. Philip Chen, Meiling Xu
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2018)
Article
Computer Science, Artificial Intelligence
Shichang Sun, Hongbo Liu, Jiana Meng, C. L. Philip Chen, Yu Yang
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2018)
Article
Computer Science, Artificial Intelligence
Jin Zhou, Long Chen, C. L. Philip Chen, Yingxu Wang, Han-Xiong Li
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2018)
Article
Automation & Control Systems
Chao Yang, Hongbo Liu, Sean McLoone, C. L. Philip Chen, Xindong Wu
IEEE TRANSACTIONS ON CYBERNETICS
(2018)
Article
Automation & Control Systems
Licheng Liu, Shutao Li, C. L. Philip Chen
IEEE TRANSACTIONS ON CYBERNETICS
(2018)