Article
Engineering, Electrical & Electronic
Kai Sun, Xinghan Xu, Nannan Lu, Huijuan Xia, Min Han
Summary: This article proposes a joint discriminative adversarial DA (JDADA) method for solving cross-domain fault diagnosis problems. The method combines domain alignment and class alignment by introducing a class alignment module and a discriminative discrepancy module to extract more discriminative features. In addition, a new pseudolabeling strategy is proposed to address the problem of target training samples without labels.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Automation & Control Systems
Qin Wang, Gabriel Michau, Olga Fink
Summary: Domain adaptation aims to improve model performance by transferring knowledge from the source domain to the target domain, but the vulnerability of domain adversarial methods to incomplete target label spaces has been demonstrated. To address this issue, a two-stage unilateral alignment approach is proposed, utilizing source domain interclass relationships to unilaterally align the target domain, which has been shown to be effective in experiments on different tasks.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Engineering, Electrical & Electronic
Xiao Yu, Hongshen Yin, Li Sun, Fei Dong, Kun Yu, Ke Feng, Yongchao Zhang, Wanli Yu
Summary: This study proposes a cross-domain bearing diagnosis framework based on transferable features and manifold embedded discriminative distribution adaption. The experimental results show that the proposed methods can achieve desirable diagnosis results and significantly outperform comparative classical transfer learning-based models when there is a class imbalance between source and target domains.
IEEE SENSORS JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Hui Tang, Yaowei Wang, Kui Jia
Summary: Unsupervised domain adaptation tackles the issue of classifying data in an unlabeled target domain while having labeled source domain data. This paper introduces a novel method called DisClusterDA, which formulates the domain adaptation problem as discriminative clustering and utilizes source data for joint training. Experimental results demonstrate that DisClusterDA outperforms existing methods on several benchmark datasets.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Shaowei Liu, Hongkai Jiang, Yanfeng Wang, Ke Zhu, Chaoqiang Liu
Summary: This paper proposes an unsupervised domain adaptation approach called Deep Feature Alignment Adaptation Network (DFAAN) to improve the domain adaptability of fault diagnosis. By aligning the latent distributions of two domains guided by a Gaussian prior, a common latent space is created to promote feature alignment. A novel discriminative reconstruction distance based on the autoencoder mechanism is introduced to narrow the discrepancy of the feature distribution. The results of diagnostic experiments show the effectiveness and versatility of the proposed approach.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Computer Science, Information Systems
Gye-Bong Jang, Jin-Young Kim, Sung-Bae Cho
Summary: This study introduces a method using machine learning technology to prevent equipment failures through adversarial learning and linking data from different domains, aiming to improve training effectiveness. A new learning method is proposed to enhance classification performance by sharing specific characteristics and improving classification accuracy in real-world applications.
Article
Computer Science, Artificial Intelligence
Wenxu Wang, Zhencai Shen, Daoliang Li, Ping Zhong, Yingyi Chen
Summary: Feature-based domain adaptation methods aim to align the distribution of samples from different domains by projecting them into the same feature space, in order to learn a transferable model. The challenge lies in reducing the domain shift and improving the discriminability of features. To address these issues, we propose a unified Probability-based Graph embedding Cross-domain and class Discriminative feature learning framework (PGCD) for unsupervised domain adaptation. We introduce novel graph embedding structures as class discriminative transfer feature learning items and cross-domain alignment items, which compact same-category samples within each domain and align the local and global geometric structures across domains. The proposed model demonstrates promising performance on benchmark datasets compared to advanced approaches, validating its effectiveness.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2023)
Article
Computer Science, Information Systems
Shanshan Li, Qingjie Zhao, Changchun Zhang, Yuanbing Zou
Summary: Domain generalization aims to generalize knowledge learned from multi-domain sources to a target domain whose statistical distribution is unknown. Most existing methods neglect the causal relationship between instances and class labels, leading to potential misclassification. To address this, we propose DDCDG, which can effectively learn causal feature representations by applying enriched data augmentation, a new regularization term for causal feature disentanglement, and centre alignment.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Rakesh Kumar Sanodiya, Leehter Yao
Summary: Transfer learning is proposed as an effective way to learn the classifier in a new domain by utilizing supervision information from related domains. Existing methods aim to minimize geometric and distribution shifts while preserving source domain discriminant information and data similarity. However, they face challenges in preserving target domain discriminant information and may consider unnecessary data for classification.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Fuqiang Liu, Wenlong Deng, Chaoqun Duan, Yi Qin, Jun Luo, Huayan Pu
Summary: This study proposes a novel duplex adversarial deep discriminative network (DADDN) for fault diagnosis in cross-domain partial transfer cases. By adopting a dual-domain adversarial attention mechanism, a new metric function of the distribution difference, and a center-of-balance weighting strategy, the accuracy and robustness of fault diagnosis are improved.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Engineering, Industrial
Pengfei Chen, Rongzhen Zhao, Tianjing He, Kongyuan Wei, Jianhui Yuan
Summary: A novel approach named joint attention adversarial domain adaptation (JAADA) is proposed to overcome the challenge of transferring feature representations. The method manually divides the extracted features into multiple regions and uses MMD to reduce the distribution discrepancy among the segmented features. Different weights obtained by the attention mechanism and MMD values are assigned to different regions, and local and global attention are fused into one unified adversarial domain adaptation framework. Comprehensive experiments show that the proposed method achieves superior convergence and improves accuracy by 1.9%, 3.0%, 2.1%, and 3.5% compared to state-of-the-art methods, respectively.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Industrial
Jinwook Lee, Myungyon Kim, Jin Uk Ko, Joon Ha Jung, Kyung Ho Sun, Byeng D. Youn
Summary: This paper proposes an AIIDA approach for fault diagnosis of rotating machinery, tackling the performance degradation caused by domain shift in training and test data. By conducting inter-domain and intra-domain alignment, the proposed method significantly improves fault diagnosis performance in the target domain.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Engineering, Electrical & Electronic
Yonghua Jiang, Zhuoqi Shi, Chao Tang, Jianfeng Sun, Linjie Zheng, Zengjie Qiu, Yian He, Guoqiang Li
Summary: This study proposes a new method called the dual domain adversarial network (DDAN) to address the issue of ineffective traditional diagnosis methods due to the feature distribution shift of rolling bearings under cross-conditions. The DDAN is integrated with a multichannel parallel feature extractor to extract features from both the frequency-domain and the time-frequency domain perspectives. The L(1,2 )norm based Wasserstein discrepancy ( L1,2 WD) is introduced to improve the stability and computation speed of the diagnosis model. A dual domain adversarial paradigm is constructed to improve the model's generalization by correcting overconfidence and expanding the confidence interval. The outcomes of verification on two bearing datasets demonstrate the excellence of DDAN in resolving cross-conditions rolling bearing fault diagnosis issues.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Electrical & Electronic
Jing Tian, Dong Wang, Liang Chen, Zhongkui Zhu, Changqing Shen
Summary: This article proposes a novel method for fault diagnosis, which achieves stable and accurate diagnosis results in different working conditions by dynamically adjusting the importance of marginal and conditional distributions. Experimental results demonstrate the effectiveness and usability of the proposed method.
IEEE SENSORS JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Yongchao Zhang, Kun Yu, Zhaohui Ren, Shihua Zhou
Summary: This article proposes a novel joint domain alignment and class alignment method for cross-domain fault diagnosis of rotating machinery, addressing the issue of misclassification near class boundaries. By combining feature extraction, MMD loss, and classifier discrepancy loss, the conditional probability discrepancy between source domain and target domain is effectively reduced.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Automation & Control Systems
Biao He, Yi Qin, Jun Luo, Fei Wu, Dengyu Xiao
Summary: In this paper, a new fast sparsity-enabled feature energy-ratio method is proposed to extract the early fault characteristics of rotating machines. This method includes two stages: adaptively segmenting the spectrum and constructing a novel index based on sparsity, energy ratio, and kurtosis to evaluate periodic impulses in each sub-signal. The refined Fourier spectrum is obtained by an improved sparse coding shrinkage denoising method, and the fault characteristics are detected using inverse fast Fourier transform and squared envelope spectra. Experimental results demonstrate the superiority of the proposed method and the robustness of the proposed index to interferences from aperiodic impulses, indicating its great potential in the fault diagnosis of rotating machines.
Article
Engineering, Mechanical
Zhihao Zhai, Shengyang Zhu, Yun Yang, Jun Luo, Chengbiao Cai
Summary: In this study, a 3D train-slab track-high pier bridge coupled dynamics model was established and verified, considering the spatial flexibility of high piers. The results show that the operation speed has a significant impact on carbody acceleration and wheel-rail force.
VEHICLE SYSTEM DYNAMICS
(2023)
Article
Automation & Control Systems
Quan Qian, Yi Qin, Jun Luo, Dengyu Xiao
Summary: In this article, a new ensemble weighting subdomain adaptation network (EWSAN) diagnostic model is proposed to improve the degree of domain confusion. The model utilizes an enhanced joint distribution alignment (EJDA) mechanism with a multiscale top classifier and ensemble voting to obtain reliable pseudolabels. An ensemble weighting maximum mean discrepancy is constructed to enhance fine-grained domain confusion. The effectiveness and superiority of the EWSAN model are validated through multiple experiments.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Automation & Control Systems
Quan Qian, Jianghong Zhou, Yi Qin
Summary: A novel relationship transfer (RT) diagnosis framework is proposed to indirectly measure and reduce the distribution discrepancy between the source domain and unseen target domain. Based on this framework, a new domain generalization transfer method called relationship transfer domain generalization network (RTDGN) is constructed. RTDGN consists of task-irrelevant domain adaptation (TIDA) and task-relevant domain generalization (TRDG).
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Dejun Xi, Lei Hou, Jun Luo, Fei Liu, Yi Qin
Summary: This paper develops a 3D gear pitting detection method based on digital twin, which includes building a geometric model and using a virtual FPP system for phase detection. An actual FPP system is also built for precise measurement. The experimental results demonstrate the superiority and effectiveness of this method.
ADVANCED ENGINEERING INFORMATICS
(2023)
Article
Engineering, Mechanical
Sheng Xiang, Yi Qin, Jun Luo, Fei Wu, Konstantinos Gryllias
Summary: In this study, a concise self-adapting deep learning network (CSDLN) is proposed for remaining useful life (RUL) prediction. The network adaptively extracts hidden features using the inverse operation of convolution and learns the features using a multi-head gated recurrent unit (MGRU). The RUL of aero-engines is determined through dimension reduction and linear regression, and the Mish activation function is used to enhance the self-adapting ability of CSDLN. The performance of CSDLN is demonstrated to be superior to the state-of-the-art RUL prediction methods in both the C-MAPSS dataset and real wind turbine gearbox bearing tests. Dropout is also applied for avoiding overfitting and quantifying the uncertainty in RUL prediction.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Mechanical
Min Wang, Xuan Fang, Yueying Wang, Jiheng Ding, Yi Sun, Jun Luo, Huayan Pu
Summary: In this study, a feedforward-feedback dual-loop active hybrid control (DAHC) strategy based on the RBF-RLS adaptive algorithm is proposed. With the assistance of the RBF neural network algorithm, the accurate online model can be identified to effectively reduce the system error caused by model inaccuracy. Experimental results show that the accuracy of the online model affects the amplitude attenuation performance by 57.1%. Real applicative experiment further proves the effectiveness of the proposed algorithm.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
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, Mechanical
Huayan Pu, Yan Jing, Xijun Cao, Xu Chen, Shujin Yuan, Jun Luo, Jinglei Zhao
Summary: This paper proposes a novel passive absolute displacement measurement system (ADMS) that utilizes the electromagnetic levitation approach. The system has a measuring amplitude range of 30 mu m- 1.2 mm with a lower frequency limit of 2 Hz. This enables a wider measuring bandwidth and higher measurement accuracy without the need for expensive and vulnerable feedback/serve-type inertia sensors. The system has promising applications in low-frequency measurements such as precision vibration isolation, anti-shake UAV platforms, seismic wave signal monitoring, ship equipment protection, and safety inspection of long-span bridges.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Mechanical
Xinru Ma, Jingyi Liu, Hengyu Li, Yonghao Xie, Tiehui Zhang, Zhaoyan Wang, Yueying Wang, Jun Liu, Shaorong Xie, Jun Luo
Summary: This paper proposes control protocols for the bidirectional formation-involved consensus of multi-agent systems (MAS) with complex behaviors. It considers multiple Lagrange systems (MLS) with uncertain parameters and bipartite topology communication networks. The validity of control protocols and the stability of MLS are confirmed by determining the consistency convergence of Lyapunov functions. The experiment section provides simulation examples consistent with the theoretical systems.
NONLINEAR DYNAMICS
(2023)
Article
Automation & Control Systems
Yi Qin, Quan Qian, Jun Luo, Huayan Pu
Summary: This study proposes a new domain adaptation mechanism called deep joint distribution alignment (DJDA) to simultaneously reduce the discrepancy in marginal and conditional distributions between source and target domains. By aligning the means and covariances and using a Gaussian mixture model and statistical metric to reduce distribution discrepancy, DJDA can achieve the highest degree of domain confusion. Experimental results demonstrate that DJDA outperforms other typical domain adaptation models in fault transfer diagnosis.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Engineering, Multidisciplinary
Jun Luo, Shengyang Zhu, Zhiping Zeng, Wanming Zhai
Summary: This paper focuses on the theoretical modeling of the track temperature field and the bond-slip behavior between track layers under thermal loading in multi-layered ballastless track systems. The temperature field is obtained through heat conduction differential equations and integral transform technique. The reliability of the model is validated by on-site experiments. Additionally, a quasi-dynamic method (QDM) is proposed to solve the interface responses, and its validity is verified by comparing results with finite element software. Numerical discussions are conducted to reveal the characteristics of the temperature field, interface damage evolution, and failure modes under time-varying thermal loads.
APPLIED MATHEMATICAL MODELLING
(2023)
Article
Engineering, Electrical & Electronic
Xuting Lan, Mingliang Zhou, Xueyong Xu, Xuekai Wei, Xingran Liao, Huayan Pu, Jun Luo, Tao Xiang, Bin Fang, Zhaowei Shang
Summary: In this paper, a framework based on two feature extraction networks and a multilevel feature fusion (MFF) network is proposed. This method can obtain multilevel degradation features and capture local and global feature information contained in these features for the prediction of distorted images. Experimental results show that the proposed method achieves greatly improved prediction accuracy and performance on five standard databases.
IEEE TRANSACTIONS ON BROADCASTING
(2023)
Article
Automation & Control Systems
Jianghong Zhou, Yi Qin, Jun Luo, Tao Zhu
Summary: Facing the gap in the unsupervised construction of health indicator (HI) with a uniform failure threshold, a new approach is developed by estimating the distribution of the raw vibration signal using the Gaussian mixture model and designing a distribution contact ratio metric (DCRM). A distribution contact ratio metric health indicator (DCRHI) is constructed to represent the degradation process and obtain a uniform failure threshold. Furthermore, a novel consolidated memory gated recurrent unit (CMGRU) is proposed to slow down the forgetting speed of important trend information and improve the prediction ability. The proposed methodology shows great application value in the RUL prediction.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
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)
