Article
Engineering, Electrical & Electronic
Jiri Sova, Petr Kolar, David Burian, Petr Vozabal
Summary: This paper proposes a method for predicting the remaining useful life (RUL) of machine tool spindle bearings using a combined calculation and experimental approach. The calculation model based on the ISO 281 standard uses real loading conditions and operation hours to calculate the spindle lifetime. The RUL is corrected using a bearing condition assessment based on vibration signal and measured data.
Article
Computer Science, Artificial Intelligence
Mohsen Motahari-Nezhad, Seyed Mohammad Jafari
Summary: This paper discusses the estimation of the remaining useful life of angular contact ball bearings using time-domain signal processing methods, introducing 60 time-domain features for fault detection and utilizing the IDE method for feature dimensionality reduction. The KNN algorithm is used for bearing classification based on selected features, with results showing high precision in fault detection. The study validates the performance of the KNN classifier with performance indices, highlighting the importance of features such as kurtosis in achieving high accuracy, precision, and specificity in bearing classification.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Engineering, Industrial
Shaoke Wan, Xiaohu Li, Yanfei Zhang, Shijie Liu, Jun Hong, Dongfeng Wang
Summary: This paper proposes a novel deep learning framework called convolutional long short-term memory fusion networks (CLSTMF) for predicting the remaining useful life (RUL) using multi-sensor data. The approach extracts shallow features from single sensor data using convolutional neural networks (CNN), captures deep temporal features using convolutional long short-term memory (CLSTM) networks, and fuses the features from different sensors using an information transfer layer (ITL). Experimental results on real run-to-failure datasets demonstrate the effectiveness and higher accuracy of the proposed approach.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Automation & Control Systems
Wentao Mao, Jiaxian Chen, Jing Liu, Xihui Liang
Summary: This article proposes a novel deep transfer learning-based online remaining useful life (RUL) approach for rolling bearings under unknown working condition. The approach solves the problem of data accumulation and bias in prediction model caused by the drift of online working condition, as well as the lack of transfer learning from offline data for online bearing's RUL prediction. The proposed approach constructs a new transfer learning-based time series recursive forecasting model to generate online RUL pseudovalues and a deep domain-adversarial regression network with multilevel adaptation to transfer prognostic knowledge and evaluate RUL values of online data batch.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Energy & Fuels
Shaotang Cai, Jun Hu, Shuoqi Ma, Zhenning Yang, Hao Wu
Summary: This paper proposes an adaptive prediction method for the remaining useful life (RUL) of electric vehicle (EV) power battery based on the whale swarm algorithm-long short-term memory (WSA-LSTM) algorithm. By screening health indicators and globally optimizing parameters, the method achieves fast and accurate prediction of battery life under DC fast charging conditions.
Article
Engineering, Multidisciplinary
Yuanyuan Sheng, Huanyu Liu, Junbao Li
Summary: Intelligent health maintenance of bearings involves constructing effective health assessment indicators and accurate remaining useful life prediction models. However, many prediction models only work under multiple constraints, and health assessment indicators may not accurately track the performance degradation of bearings. This study proposes a method that assesses bearing performance degradation and predicts remaining life. It constructs a family of health evaluation indices based on generalized power mean and high-order origin moments, and uses an improved Paris-Erdogan model with the optimal health evaluation index as input to predict remaining life. Experimental results show that the proposed method outperforms traditional statistical indicators and prediction models in terms of tracking accuracy and prediction error.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2023)
Article
Engineering, Multidisciplinary
Chong Chen, Tao Wang, Ying Liu, Lianglun Cheng, Jian Qin
Summary: This paper proposes a novel deep learning algorithm called SAConvFormer based on raw vibration data for accurate prediction of the remaining useful life (RUL) of bearings, without the need for prior knowledge or feature engineering. The experimental results demonstrate the significant advantages of this algorithm in terms of prediction accuracy compared to other algorithms.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2022)
Article
Automation & Control Systems
Tangfan Xiahou, Zhiguo Zeng, Yu Liu
Summary: This article introduces a MoG-EHMM model that fuses expert knowledge and condition monitoring information for RUL prediction, demonstrating that the performance of RUL prediction can be substantially improved by incorporating expert knowledge with monitoring information.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Engineering, Electrical & Electronic
Chaoying Yang, Jie Liu, Kaibo Zhou, Xingxing Jiang, Ming-Feng Ge, Yiben Liu
Summary: A node-level PathGraph-based method for bearing remaining useful life (RUL) prediction is proposed, which enhances graph feature learning ability by combining Chebyshev graph convolutional network (ChebGCN) with bidirectional long short-term memory network (BiLSTM), and inputting different chronological PathGraphs related to bearings' states.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Engineering, Multidisciplinary
Guopeng Zhu, Zening Zhu, Ling Xiang, Aijun Hu, Yonggang Xu
Summary: A novel model called dynamically activated convolutional network (DACN) is proposed to extract the nonlinear information of vibration signal, and DACN-ConvLSTM model is used for the prediction of rolling bearing's remaining useful life (RUL). Experimental results demonstrate that the DACN-ConvLSTM method outperforms other methods in terms of RUL prediction accuracy and maintains prediction performance on different datasets.
Article
Engineering, Multidisciplinary
Lu Liu, Xiao Song, Kai Chen, Baocun Hou, Xudong Chai, Huansheng Ning
Summary: In recent years, data-driven approaches for remaining useful life (RUL) prognostics have gained widespread attention. This paper explores the degradation process of bearings and proposes an enhanced encoder-decoder framework for accurately predicting the remaining useful life of bearings by using trigonometric functions and cumulative operations to enhance the quality of health indicators. The experimental results demonstrate the superiority and feasibility of the proposed method compared to several state-of-the-art methods.
Article
Energy & Fuels
Tarek Berghout, Leila-Hayet Mouss, Toufik Bentrcia, Mohamed Benbouzid
Summary: This study introduces a novel deep learning algorithm that enhances generalization capacity by transferring knowledge from life cycles of similar systems for health status prognosis. Experimental validation demonstrates the capacity and performance advantage of the proposed approach.
IEEE TRANSACTIONS ON ENERGY CONVERSION
(2022)
Article
Computer Science, Artificial Intelligence
Yifei Ding, Minping Jia, Jichao Zhuang, Peng Ding
Summary: This paper proposes a novel framework for learning imbalanced regression using cost-sensitive learning and deep feature transfer. It fills the research gap in bearing remaining useful life estimation with imbalanced data. The framework incorporates techniques such as discretization and label distribution smoothing, deep feature transfer via CORrelation ALignment (CORAL), and cost-sensitive learning via class-balanced re-weighting. The effectiveness of the framework is demonstrated through the design of various imbalanced bearing training sets and comparison with other methods.
APPLIED SOFT COMPUTING
(2022)
Review
Engineering, Mechanical
Jiaxian Chen, Ruyi Huang, Zhuyun Chen, Wentao Mao, Weihua Li
Summary: Accurate remaining useful life (RUL) prediction for rolling bearings is a challenging task due to complex degradation processes, varying working conditions, and insufficient run-to-failure data. Transfer learning (TL) has shown promising results in addressing these challenges. This paper aims to comprehensively review TL-based RUL prediction methods and applications, discussing the problem definitions, general procedure, algorithms, and challenges in the field.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Computer Science, Artificial Intelligence
Tae San Kim, So Young Sohn
Summary: The research proposes a multi-task learning method based on convolution neural networks to better reflect the relationship between remaining useful life estimation and health status detection process, and it shows superior performance to existing baseline models in experiments using the C-MAPSS dataset for aero-engine unit prognostics.
JOURNAL OF INTELLIGENT MANUFACTURING
(2021)
Article
Engineering, Mechanical
Shuai Gao, Qinkai Han, Ningning Zhou, Paolo Pennacchi, Fulei Chu
Summary: Experimental study was conducted on the stability and skidding degree of the porous oil-containing polyimide cage used in spacecraft rolling bearings. The results showed that the oil-content ratio, applied load, and rotation speed all influenced the vibration level, skidding ratio, and whirl radius fluctuation amplitude of the cage.
TRIBOLOGY INTERNATIONAL
(2022)
Article
Engineering, Mechanical
Shuai Gao, Steven Chatterton, Paolo Pennacchi, Qinkai Han, Fulei Chu
Summary: This paper presents a KH-TEHD model to address the damage problems introduced by skidding behavior and validates its accuracy and effectiveness through experimental studies. The results show that the degree of overskidding is approximately proportional to the square of the bearing size, and the maximum thermal deformation at the bearing raceway under starvation lubrication reaches 4 times the ball-raceway clearance.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Chemistry, Analytical
Syed Muhammad Tayyab, Steven Chatterton, Paolo Pennacchi
Summary: This paper presents a defect diagnosis method for rolling element bearings based on CNN and order maps, which can accurately diagnose faults under variable speed and load conditions.
Article
Engineering, Mechanical
He Biao, Yi Qin, Jun Luo, Weixin Yang, Lang Xu
Summary: In this paper, a fault feature extraction methodology based on an adaptive spectrum segmentation method, a new voting index, and a new variational model is proposed. The results show that this method outperforms other classical methods in extracting fault characteristics from vibration signals.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Electrical & Electronic
Federico Longoni, Anders Hagglund, Francesco Ripamonti, Paolo L. M. Pennacchi
Summary: The powertrain of a car plays a crucial role in the comfort offered by the vehicle due to vibrations. This paper focuses on identifying quantities that correlate with the vibration performance of powertrain setups and proposes a method to test the specificity of requirements.
Article
Engineering, Electrical & Electronic
Syed Muhammad Tayyab, Steven Chatterton, Paolo Pennacchi
Summary: Convolutional neural networks (CNN) have excellent image recognition characteristics and can be used for defect diagnosis tasks in image processing. This study compares different image processing methods in terms of fault detection accuracy and computational expense, and proposes a hybrid-ensemble method involving decision-level fusion that is computationally less expensive. The performance of these models is also compared with minimal training data availability and slight variation in operating conditions.
Article
Engineering, Electrical & Electronic
Edoardo Gheller, Steven Chatterton, Andrea Vania, Paolo Pennacchi
Summary: This paper investigates the modeling of squeeze film dampers (SFDs) and highlights the key phenomena involved in their characterization, including oil film cavitation, air ingestion, and inertia. A numerical model based on the Reynolds equation is introduced and validated with experimental results. Different designs and configurations of the feeding and sealing system are considered, and an example of applying SFDs to a compressor rotor is proposed.
Article
Engineering, Electrical & Electronic
Davide Massocchi, Marco Lattuada, Steven Chatterton, Paolo Pennacchi
Summary: This study aims to optimize lubrication systems and achieve energy efficiency and CO2 emissions reduction. By using a specific tribological testing procedure, significant time and operational cost savings can be achieved, with good correlation to the FZG test results.
Article
Engineering, Electrical & Electronic
Steven Chatterton, Edoardo Gheller, Andrea Vania, Paolo Pennacchi, Phuoc Vinh Dang
Summary: This paper investigates the static characteristics of tilting-pad journal bearings lined with different materials. A full 3D thermal model is used to evaluate the temperature distribution in the bearing, while a finite element model is used to evaluate the deformation of the pads. The permissible operating range in terms of load and speed for each material is defined by considering the limits on the maximum temperature, permissible mechanical stress, and minimum oil-film thickness.
Article
Engineering, Electrical & Electronic
Paolo Pennacchi, Steven Chatterton, Andrea Vania, Davide Massocchi
Summary: This paper focuses on applying signal processing techniques to vibration data for defining damage indices for railway axle bearings. The experimental data were obtained following the test specifications of the standard EN 12082:2017.
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)
