Article
Engineering, Mechanical
Feng Yang, Mohamed Salahuddin Habibullah, Yan Shen
Summary: This paper proposed a generic prognostics framework with HI dynamic smoothing and multi-model ensemble realization, which enables the incorporation of different types of HI degradations. Experimental studies on real data from 8 induction motors showed that the proposed prognostic method using nonlinearly degrading HI resulted in clear performance improvements compared to linear HI degradation prediction.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Engineering, Mechanical
Yang Chang, Jianxiao Zou, Shicai Fan, Chao Peng, Huajing Fang
Summary: This paper proposes a prognostic technique with the capability of uncertainty management, which consists of two phases to reduce the uncertainty and ensure the reliability of the prognostic result.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
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
Computer Science, Information Systems
Antonio Leonel Hernandez Martinez, Saqib Khursheed, Turki Alnuayri, Daniele Rossi
Summary: This paper proposes a novel methodology for online prediction of remaining useful lifetime (RUL) in high reliability and safety electronic systems using support vector regression (SVR) model. The methodology utilizes frequency degradation as a trackable path and depends on temperature, voltage, and aging. Results show that the methodology can accurately estimate RUL with a high level of accuracy.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING
(2022)
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, Industrial
Ji-Yan Wu, Min Wu, Zhenghua Chen, Xiaoli Li, Ruqiang Yan
Summary: This study proposes a joint classification-regression scheme for multi-stage RUL prediction, which classifies system health stages based on real-time sensory data and trained models, and performs stage-level RUL prediction with regression algorithms. Experimental results show that the proposed method achieves approximately 6.5% accuracy improvement over state-of-the-art algorithms in RUL prediction.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
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
Yimin Jiang, Tangbin Xia, Xiaolei Fang, Dong Wang, Ershun Pan, Lifeng Xi
Summary: Infrared thermography is used for contactless machine health monitoring by capturing real-time degradation temperature information. This article introduces a sparse hierarchical parallel residual networks ensemble (SHPRNE) method to address the challenge of multiscale characteristics and spatiotemporal degradation discrepancy in infrared images. The proposed method utilizes parallel multiscale kernels and a hierarchical residual connection procedure to capture complementary degradation patterns and promote interactivity between different levels of features. Additionally, a sparse ensemble algorithm, integrated with network pruning and local minima perpetuation, is used to derive diverse networks and improve generalization.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Tao Jing, Pai Zheng, Liqiao Xia, Tianyuan Liu
Summary: This article presents an interpretable RUL prediction method based on TF-SCN, HLS-VAE, and a regressor. Experimental results demonstrate that the proposed approach achieves high-quality RUL prediction while providing a visual latent space for evaluating RUL degradation patterns.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Computer Science, Hardware & Architecture
Haosen Yang, Keqin Ding, Robert C. Qiu, Tiebin Mi
Summary: The paper introduces a seq2seq learning method with embedded normalizing flow to improve the prediction accuracy of RUL for assets or systems. This method enhances the representation ability for nonlinearity between input sequential data and outputs, making the model more suitable for vibration signal analysis.
IEEE TRANSACTIONS ON RELIABILITY
(2021)
Article
Computer Science, Artificial Intelligence
Xiang Li, Wei Zhang, Hui Ma, Zhong Luo, Xu Li
Summary: This paper proposes a deep learning-based RUL prediction method, which aligns the data of different entities in similar degradation levels through a cycle-consistent learning scheme to improve prediction performance. Experimental results suggest that the method offers a novel perspective on RUL estimations.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Engineering, Multidisciplinary
David A. Najera-Flores, Zhen Hu, Mayank Chadha, Michael D. Todd
Summary: In order to predict the remaining useful life (RUL) of lithium-ion batteries, simplified physical laws and machine learning-based methods can be used to develop a capacity degradation model. While simplified physical models are easy to implement, they may result in large errors in failure prognostics. Data-driven models can provide more accurate degradation forecasting but may require a large amount of training data and may produce predictions inconsistent with physical laws. Existing methods also face challenges in predicting RUL at the early stages of battery life.
APPLIED MATHEMATICAL MODELLING
(2023)
Article
Engineering, Mechanical
Luca Viale, Alessandro Paolo Daga, Alessandro Fasana, Luigi Garibaldi
Summary: This paper proposes a novel data-driven method to enhance the accuracy of predicting the remaining useful life (RUL) of aircraft engines in real-time prognostic systems. The method considers multiple degradation mechanisms and is easy to implement. It combines a modified k-Nearest Neighbors Interpolation (kNNI) with an a posteriori Least Square Smoothing (LSS) that is automatically optimized for minimizing prediction error. The method was validated using a new NASA dataset and compared to a reference kNN-based method to demonstrate its superiority in terms of results and performance improvements.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Automation & Control Systems
Telmo Fernandez De Barrena, Juan Luis Ferrando, Ander Garcia, Xabier Badiola, Mikel Saez de Buruaga, Javier Vicente
Summary: In the manufacturing industry, monitoring the health of critical components, such as cutting tools in the machine tool sector, is crucial for tackling challenges related to production quality, productivity, and energy consumption. This paper focuses on the prediction of the remaining useful life (RUL) of cutting tools, which is important for optimizing maintenance strategies. The study evaluates various signals captured from machine tools to identify the optimum predictors for RUL prediction and investigates the use of bidirectional recurrent neural networks (BRNN) as regression models. The results show that the root mean squared (RMS) parameter of the forward force (F-y) signal performs the best for RUL prediction.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2023)
Article
Energy & Fuels
Adam Thelen, Meng Li, Chao Hu, Elena Bekyarova, Sergey Kalinin, Mohan Sanghadasa
Summary: Traditional model-based approaches for predicting the remaining useful life (RUL) of a rechargeable battery cell tend to fail when the capacity fade trend changes over time. To improve the accuracy of RUL prediction, we propose an approach that combines empirical model-based prediction with data-driven error correction. Experimental results show that the data-driven error correction effectively reduces prediction errors and provides more conservative uncertainty estimates.
Article
Engineering, Mechanical
Xuanen Kan, Yanjun Lu, Fan Zhang, Weipeng Hu
Summary: A blade disk system is crucial for the energy conversion efficiency of turbomachinery, but differences between blades can result in localized vibration. This study develops an approximate symplectic method to simulate vibration localization in a mistuned bladed disk system and reveals the influences of initial positive pressure, contact angle, and surface roughness on the strength of vibration localization.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Zimeng Liu, Cheng Chang, Haodong Hu, Hui Ma, Kaigang Yuan, Xin Li, Xiaojian Zhao, Zhike Peng
Summary: Considering the calculation efficiency and accuracy of meshing characteristics of gear pair with tooth root crack fault, a parametric model of cracked spur gear is established by simplifying the crack propagation path. The LTCA method is used to calculate the time-varying meshing stiffness and transmission error, and the results are verified by finite element method. The study also proposes a crack area share index to measure the degree of crack fault and determines the application range of simplified crack propagation path.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Rongjian Sun, Conggan Ma, Nic Zhang, Chuyo Kaku, Yu Zhang, Qirui Hou
Summary: This paper proposes a novel forward calculation method (FCM) for calculating anisotropic material parameters (AMPs) of the motor stator assembly, considering structural discontinuities and composite material properties. The method is based on multi-scale theory and decouples the multi-scale equations to describe the equivalence and equivalence preconditions of AMPs of two scale models. The effectiveness of this method is verified by modal experiments.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Hao Zhang, Jiangcen Ke
Summary: This research introduces an intelligent scheduling system framework to optimize the ship lock schedule of the Three Gorges Hub. By analyzing navigational rules, operational characteristics, and existing problems, a mixed-integer nonlinear programming model is formulated with multiple objectives and constraints, and a hybrid intelligent algorithm is constructed for optimization.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Jingjing He, Xizhong Wu, Xuefei Guan
Summary: A sensitivity and reliability enhanced ultrasonic method has been developed in this study to monitor and predict stress loss in pre-stressed multi-layer structures. The method leverages the potential breathing effect of porous cushion materials in the structures to increase the sensitivity of the signal feature to stress loss. Experimental investigations show that the proposed method offers improved accuracy, reliability, and sensitivity to stress change.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Benyamin Hosseiny, Jalal Amini, Hossein Aghababaei
Summary: This paper presents a method for monitoring sub-second or sub-minute displacements using GBSAR signals, which employs spectral estimation to achieve multi-dimensional target detection. It improves the processing of MIMO radar data and enables high-resolution fast displacement monitoring from GBSAR signals.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xianze Li, Hao Su, Ling Xiang, Qingtao Yao, Aijun Hu
Summary: This paper proposes a novel method for bearing fault identification, which can accurately identify faults with few samples under complex working conditions. The method is based on a Transformer meta-learning model, and the final result is determined by the weighted voting of multiple models.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xiaomeng Li, Yi Wang, Guangyao Zhang, Baoping Tang, Yi Qin
Summary: Inspired by chaos fractal theory and slowly varying damage dynamics theory, this paper proposes a new health monitoring indicator for vibration signals of rotating machinery, which can effectively monitor the mechanical condition under both cyclo-stationary and variable operating conditions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Hao Wang, Songye Zhu
Summary: This paper extends the latching mechanism to vibration control to improve energy dissipation efficiency. An innovative semi-active latched mass damper (LMD) is proposed, and different latching control strategies are tested and evaluated. The latching control can optimize the phase lag between control force and structural response, and provide an innovative solution to improve damper effectiveness and develop adaptive semi-active dampers.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Menghao Ping, Xinyu Jia, Costas Papadimitriou, Xu Han, Chao Jiang, Wang-Ji Yan
Summary: Identification of non-Gaussian processes is a challenging task in engineering problems. This article presents an improved orthogonal series expansion method to convert the identification of non-Gaussian processes into a finite number of non-Gaussian coefficients. The uncertainty of these coefficients is quantified using polynomial chaos expansion. The proposed method is applicable to both stationary and nonstationary non-Gaussian processes and has been validated through simulated data and real-world applications.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Lei Li, Wei Yang, Dongfa Li, Jianxin Han, Wenming Zhang
Summary: The frequency locking phenomenon induced by modal coupling can effectively overcome the dependence of peak frequency on driving strength in nonlinear resonant systems and improve the stability of peak frequency. This study proposes the double frequencies locking phenomenon in a three degrees of freedom (3-DOF) magnetic coupled resonant system driven by piezoelectricity. Experimental and theoretical investigations confirm the occurrence of first frequency locking and the subsequent switching to second frequency locking with the increase of driving force. Furthermore, a mass sensing scheme for double analytes is proposed based on the double frequencies locking phenomenon.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Kai Ma, Jingtao Du, Yang Liu, Ximing Chen
Summary: This study explores the feasibility of using nonlinear energy sinks (NES) as replacements for traditional linear tuned mass dampers (TMD) in practical engineering applications, specifically in diesel engine crankshafts. The results show that NES provides better vibration attenuation for the crankshaft compared to TMD under different operating conditions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Wentao Xu, Li Cheng, Shuaihao Lei, Lei Yu, Weixuan Jiao
Summary: In this study, a high-precision hydraulic mechanical stand and a vertical mixed-flow pumping station device were used to conduct research on cavitation signals of mixed-flow pumps. By analyzing the water pressure pulsation signal, it was found that the power spectrum density method is more sensitive and capable of extracting characteristics compared to traditional time-frequency domain analysis. This has significant implications for the identification and prevention of cavitation in mixed-flow pump machinery.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xiaodong Chen, Kang Tai, Huifeng Tan, Zhimin Xie
Summary: This paper addresses the issue of parasitic motion in microgripper jaws and its impact on clamping accuracy, and proposes a symmetrically stressed parallelogram mechanism as a solution. Through mechanical modeling and experimental validation, the effectiveness of this method is demonstrated.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Zhifeng Shi, Gang Zhang, Jing Liu, Xinbin Li, Yajun Xu, Changfeng Yan
Summary: This study provides useful guidance for early bearing fault detection and diagnosis by investigating the effects of crack inclination and propagation direction on the vibration characteristics of bearings.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)