Article
Engineering, Mechanical
Atik Faysal, Wai Keng Ngui, M. H. Lim
Summary: The proposed NEEEMD method aims to further reduce white noise and select sensitive mode functions to enhance fault-related impulses through a combination of time and frequency domain characteristics. The application of MOMEDA filter improves fault diagnosis accuracy by identifying more fault characteristic impulses.
JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES
(2021)
Article
Engineering, Multidisciplinary
Jinde Zheng, Miaoxian Su, Wanming Ying, Jinyu Tong, Ziwei Pan
Summary: The study introduces the improved Uniform Phase Empirical Mode Decomposition (IUPEMD) method, which enhances the accuracy and performance of signal decomposition by adaptively selecting the amplitude of the sinusoidal wave and choosing the optimal result based on orthogonality index.
Article
Engineering, Multidisciplinary
Cheng Zhong, Jie-Sheng Wang, Wei-Zhen Sun
Summary: A novel fault diagnosis method for rotating bearings, based on the analysis of bearing rotating speed feature and vibration analysis technique, was proposed. The method utilizes improved EEMD and DBN algorithms to decompose vibration data, eliminate interference signals, and extract data features for fault diagnosis.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2022)
Article
Engineering, Mechanical
Robert B. Randall, Jerome Antoni
Summary: Empirical mode decomposition (EMD) is a method to decompose complex signals into intrinsic mode functions (IMFs) which can be considered as carrier frequencies modulated in amplitude and phase. This paper shows that EMD is not suitable for rolling element bearing (REB) signals due to their intrinsic properties. Instead, other methods based on relevant criteria should be used to extract the desired diagnostic information. The paper also discusses the limitations of EMD, such as end effects and mode mixing, which make it difficult to guarantee repeatable results. The poor selectivity of the bandpass filters given by EMD is compared to alternative filters.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Multidisciplinary
Rui Yuan, Yong Lv, Zhiwen Lu, Si Li, Hewenxuan Li
Summary: This paper proposes a novel approach to fault diagnosis of rotary machinery using phase space reconstruction of intrinsic mode functions and neural network under various operating conditions. The proposed approach has been validated by theoretical derivations, numerical simulations and experimental data, showing promising results in fault diagnosis of rotary machinery.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Yun Li, Jiwen Zhou, Hongguang Li, Guang Meng, Jie Bian
Summary: In this article, a fast and adaptive empirical mode decomposition method (FAEMD) is proposed to address the limitations of the original EMD method, such as differences in white noise amplitude, number of trials, and low computational efficiency. FAEMD combines the advantages of the order statistics filter (OSF) with the original EMD to effectively extract key feature information from fault signals with low calculation cost.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Multidisciplinary
Sumair Aziz, Muhammad Umar Khan, Muhammad Faraz, Gabriel Axel Montes
Summary: This article introduces a new vibration signal preprocessing scheme called AREEMD and a novel cAR feature extraction method. AREEMD generates modes based on relative energy and automates signal reconstruction, while cAR features utilize cepstrum information to provide discriminant markers for different machine faults. The weighted K-nearest neighbor algorithm achieved the best results when validated on four different datasets. The proposed scheme demonstrated promising performances with high accuracy rates on various datasets, showcasing its significant generalizability and ability to detect faults early and reduce system shutdowns caused by bearing failure.
Article
Engineering, Mechanical
Yongjian Sun, Shaohui Li, Yaling Wang, Xiaohong Wang
Summary: The proposed method decomposes the vibration signal of rolling bearings into Intrinsic Mode Functions, transforms the first five functions into snowflake images, calculates feature matrices, and extracts improved Manhattan distance for signal classification.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Business, Finance
Kunliang Xu, Weiqing Wang
Summary: A reliable crude oil price forecast is crucial for market pricing. This study incorporates a rolling window into two prevalent EEMD-based modeling paradigms to improve accuracy. The results show that EEMD plays a weak role in improving crude oil price forecasts when only the in-sample set is preprocessed, but the rolling EEMD-denoising model has an advantage for long-term forecasting.
INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS
(2023)
Article
Engineering, Multidisciplinary
Anil Kumar, Yaakoub Berrouche, Radoslaw Zimroz, Govind Vashishtha, Sumika Chauhan, C. P. Gandhi, Hesheng Tang, Jiawei Xiang
Summary: A non-parametric complementary ensemble empirical mode decomposition (NPCEEMD) method is proposed for identifying bearing defects using weak features. Unlike existing decomposition methods, NPCEEMD does not require defining the ideal SNR of noise and the number of ensembles each time while processing the signals. Simulation results show that NPCEEMD has less mode mixing compared to existing decomposition methods. After conducting in-depth simulation analysis, the proposed method is applied to experimental data. The steps of the NPCEEMD method include obtaining the raw signal, decomposing the obtained signal, computing the mutual information (MI) of the raw signal with NPCEEMD-generated IMFs, selecting IMFs with MI above 0.1, combining them to form a resulting signal, and computing the envelope spectrum of the resulting signal to confirm the presence of defect.
Article
Chemistry, Multidisciplinary
Manuel A. A. Centeno-Bautista, Angel H. H. Rangel-Rodriguez, Andrea V. V. Perez-Sanchez, Juan P. P. Amezquita-Sanchez, David Granados-Lieberman, Martin Valtierra-Rodriguez
Summary: Sudden cardiac death is a significant global health problem, accounting for 15-20% of global deaths. A research proposes a methodology combining complete ensemble empirical mode decomposition (CEEMD) and convolutional neural network (CNN) to predict SCD events 30 minutes in advance with 97.5% accuracy. The study compares the results with ensemble empirical mode decomposition (EEMD) and empirical mode decomposition (EMD) methods.
APPLIED SCIENCES-BASEL
(2023)
Article
Engineering, Mechanical
Qiuyu Song, Xingxing Jiang, Guifu Du, Jie Liu, Zhongkui Zhu
Summary: This paper proposes a smart multichannel mode extraction (SMME) method for enhanced bearing fault diagnosis. The SMME method based on multivariate variational mode decomposition (MVMD) and manifold learning shows good performance in mining the intrinsic nonlinear and nonstationary features from multichannel modes of different quality.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Multidisciplinary
Danchen Zhu, Guoqiang Liu, Xingyu Wu, Bolong Yin
Summary: To address the problem of bearing fault signals being contaminated by strong background interference, an enhanced empirical Fourier decomposition technique was proposed. The method includes a trend-line-extraction-based method to weaken the influence of transmission path, a correlation-coefficient-based decomposition number selection approach to avoid irrelevant modal functions, and a band improvement strategy to reduce invalid frequency bands. The results show that this method effectively extracts fault characteristics from strong background interference.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Engineering, Multidisciplinary
Kaicheng Zhao, Junqing Xiao, Chun Li, Zifei Xu, Minnan Yue
Summary: This study proposes a novel adaptive decomposition algorithm based on CEEMDAN and fractal dimension to overcome limitations in traditional EMD-based algorithms such as redundancy and mode confusion. An intelligent fault diagnosis model is developed using CNN and the proposed CEEMDAN to enhance rolling bearing state recognition. The methodology is validated through empirical experiments involving rolling bearings, and its superiority and reliability are compared with CNN-based approaches.
Article
Acoustics
Lei Li, Qian Wang, Xin Qing, Gang Qiao, Xinyu Liu, Songzuo Liu
Summary: This study proposes a robust unsupervised whistle enhancement scheme based on improved local mean decomposition, which plays an important role in studying dolphin behavior and population distributions. Experimental results demonstrate that the proposed scheme outperforms other compared whistle enhancement schemes under different signal-to-noise ratios.
JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA
(2022)
Article
Automation & Control Systems
Houlian Wang, Gongbo Zhou, Laksh Bhatia, Zhencai Zhu, Wei Li, Julie A. McCann
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2020)
Article
Acoustics
Xiumei Liu, Jie He, Beibei Li, Chi Zhang, Huawen Xu, Wei Li, Fangwei Xie
Summary: The unsteady cavitation flow in the regulating valve can be divided into fixed and travelling cavitation bubbles, and the length/radius ratio of the valve spool affects the flow characteristics.
SHOCK AND VIBRATION
(2021)
Article
Engineering, Electrical & Electronic
Xiaodong Yan, Gongbo Zhou, Wei Wang, Ping Zhou, Zhenzhi He
Summary: This paper proposes a SOC estimation method based on LSTM network and improved particle filter. It utilizes the timing characteristics of data and optimization algorithms to achieve accurate estimation of SOC for lithium-ion batteries.
IEEE SENSORS JOURNAL
(2022)
Article
Engineering, Multidisciplinary
Zhou Ping, Zhang Chuangchuang, Zhou Gongbo, He Zhenzhi, Yan Xiaodong, Wang Shihao, Sun Meng, Hu Bing
Summary: An automatic method for detecting defects on the whole surface of bearing rings based on machine vision is proposed in order to solve the problem of multiple defects distributed on multiple surfaces and difficult manual detection. This method includes analyzing the characteristics of surface defects, designing an efficient scheme for acquiring the whole surface image, developing a method for defect detection, and optimizing the detection strategy. The experimental results show that the proposed method has a comprehensive accuracy of 95%, meeting the detection requirements.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Ping Zhou, Gongbo Zhou, Shihao Wang, Hanyu Wang, Zhenzhi He, Xiaodong Yan
Summary: This article proposes an intelligent detection method of steel wire rope (SWR) surface damage based on an improved version of the You Only Look Once (YOLO) algorithm, called WR-YOLO. The WR-YOLO algorithm achieves better performance in terms of detection speed and accuracy compared to other YOLO algorithms. Experimental results demonstrate its effectiveness in detecting surface damage and its capability for dynamic detection.
IEEE SENSORS JOURNAL
(2022)
Article
Automation & Control Systems
Houlian Wang, Gongbo Zhou, Jing Xu, Zhiqiang Liu, Xiaodong Yan, Julie A. McCann
Summary: This article proposes a simplified historical-information-based state of charge (SOC) prediction algorithm to address range anxiety and save computing resources for electric vehicles. Experimental results demonstrate that the algorithm has high prediction accuracy and shorter running time.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Robotics
Xin Shu, Chaoquan Tang, Gongbo Zhou, Ping Zhou, Lulu Sun, Xiaodong Yan
Summary: This research proposes a gait-based control method that allows snake robots to move in different environments. The flexible gait transition motion improves its application in complex environments. To enhance the flexibility of gait-based control, various gait transition motions are introduced, expanding the gait transition network for snake robots. A cost function is designed to guide parameter conversion within the parameterized gaits based on the sine wave function. Furthermore, several experiments are conducted to validate the effectiveness of the proposed transition motion.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Chemistry, Multidisciplinary
Chaoquan Tang, Erfei Gao, Yingming Li, Menggang Li, Deen Bai, Hongwei Tang, Gongbo Zhou
Summary: The coal mine wind shaft is an important ventilation channel in coal mines, and its long-term safety is crucial. However, the current manual inspection of wind shafts has low reliability and high risk. The two main problems in shaft wall detection are the high humidity and dust concentration in the ventilation shafts, making imaging difficult, and the long and irregular cracks on the shaft wall. To address these issues, experiments were conducted to determine the mapping analysis between water vapor and dust concentration and image definition. A robot was designed to move along the axial and circumferential directions to approach the shaft wall, and a crack parameter detection method based on deep learning was used to control the robot's movement direction according to the crack direction.
APPLIED SCIENCES-BASEL
(2023)
Article
Engineering, Multidisciplinary
Chaoquan Tang, Lulu Sun, Gongbo Zhou, Xin Shu, Hongwei Tang, Hao Wu
Summary: Gait generation method is important in snake robot motion control. The MCC method simplifies and unifies the control functions of different snake robots gaits by extracting the main features of the backbone curves of snake robots gaits. Based on this method, some snake robot gaits are reconstructed, and an AEH-sidewinding gait control method is proposed. The unified gait expression of snake robots will be helpful in achieving smooth gait switching between different gaits.
Article
Environmental Sciences
Menggang Li, Kun Hu, Yuwang Liu, Eryi Hu, Chaoquan Tang, Hua Zhu, Gongbo Zhou
Summary: This paper proposes a multimodal robust SLAM method for accurate pose estimation and scene reconstruction in complex underground coal mine environments. The method utilizes wireless beacon-assisted geographic information transmission and lidar-IMU-UWB elastic fusion mechanism to achieve consistency with absolute geographic information. Extensive field tests demonstrate the practical robustness and real-time performance of the method in various underground application scenarios.
Article
Engineering, Electrical & Electronic
Wei Wang, Gongbo Zhou, Guoqing Ma, Xiaodong Yan, Ping Zhou, Zhenzhi He, Tianbing Ma
Summary: This article proposes a novel competitive temporal convolutional network (CTCN) for predicting the remaining useful life (RUL) of rolling bearings. The deep learning model's feature extraction is enhanced by a novel dual competitive attention (DCA) mechanism. The experimental results demonstrate that the proposed CTCN is effective and the DCA significantly improves the accuracy of RUL prediction. Additionally, the proposed global competition (GC) and multidimensional competition (MC) modules improve the performance of the attention mechanism.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Electrical & Electronic
Xiaodong Yan, Gongbo Zhou, Ping Zhou, Wei Wang, Lianfeng Han, Zhenzhi He
Summary: This paper proposes a scraper conveyor chain tension monitoring network model based on data priority, and a power allocation method for node transmission based on Lyapunov. It effectively meets the stability and priority requirements of the network data queue.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Electrical & Electronic
Zhenzhi He, Xiaodong Yan, Yuan Sun, Wei Wang, Gongbo Zhou, Ping Zhou, Chaoquan Tang, Fan Jiang
Summary: This article proposes a wear detection method based on flexible printed circuits for detecting wear on sliding sleeves in mines. By designing a new sensor and establishing a wear detection system, the relationship between the number of broken wires and wear value is determined. Verification experiments confirm the feasibility of the proposed approach.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Computer Science, Information Systems
Shengfei Ji, Wei Li, Bo Zhang, Lingwei Zhou, Chenxi Duan
Summary: This study developed a vision-based bucket teeth fault detection algorithm with deep learning, which effectively improves the accuracy and speed of bucket teeth detection on electric shovels, solving the problem of premature breakage and falling off of bucket teeth. The Faster R-CNN model outperformed other models such as ZFNet, ResNet-50, and VGG16 in terms of accuracy and speed.
Article
Engineering, Electrical & Electronic
Ping Zhou, Gongbo Zhou, Houlian Wang, Dongxu Wang, Zhenzhi He
Summary: The study proposes a deep learning-based VPT framework, WR-IPDCNN, for efficient detection of surface damage on wire ropes. By using image preprocessing and deep convolutional neural network, the framework achieves high accuracy in detection and significant improvement in automation level.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Acoustics
Sandip Chajjed, Mohammad Khalil, Dominique Poirel, Chris Pettit, Abhijit Sarkar
Summary: This paper reports the generalization of the Bayesian formulation of the flutter margin method, which improves the predictive performance by incorporating the joint prior of aeroelastic modal parameters. The improved algorithm reduces uncertainties in predicting flutter speed and can cut cost by reducing the number of flight tests.
JOURNAL OF SOUND AND VIBRATION
(2024)
Article
Acoustics
Pascal Zeise, Bernhard Schweizer
Summary: Air ring bearings are an improved version of classical air bearings, providing better damping behavior and allowing operation above the linear threshold speed of instability. However, there is a risk of dangerous vibrations in certain rotor systems, which can be addressed by considering ring tilting effects.
JOURNAL OF SOUND AND VIBRATION
(2024)
Article
Acoustics
Zbynek Sika, Jan Krivosej, Tomas Vyhlidal
Summary: This paper presents a novel design of a compact six degrees of freedom active vibration absorber with six identical eigenfrequencies. The objective is to completely suppress the vibration of a machine structure with six motion components. By utilizing a Stewart platform structure equipped with six active legs, a spatial unifrequency absorber with six identical eigenfrequencies is achieved. The design is optimized using a correction feedback and active delayed resonator feedback.
JOURNAL OF SOUND AND VIBRATION
(2024)
Article
Acoustics
Kai Li, Yufeng Liu, Yuntong Dai, Yong Yu
Summary: This paper presents a novel light-powered self-oscillating liquid crystal elastomer (LCE) bow that can self-oscillate continuously and periodically under steady illumination. The dynamics of the LCE bow are theoretically investigated and numerical calculations predict its motion regimes. The suggested LCE bow offers potential advantages in terms of simple structure, customizable size, flexible regulation, and easy assembly.
JOURNAL OF SOUND AND VIBRATION
(2024)
Article
Acoustics
Carmelo Rosario Vindigni, Giuseppe Mantegna, Calogero Orlando, Andrea Alaimo
Summary: In this study, a simple adaptive flutter suppression system is designed to increase the operative speed range of a wing-aileron aeroelastic plant. The system achieves almost strictly passivity by using a parallel feed-forward compensator implementation and the controller parameters are optimized using a population decline swarm optimization algorithm. Numerical simulations prove the effectiveness of the proposed simple adaptive flutter suppression architecture in different flight scenarios.
JOURNAL OF SOUND AND VIBRATION
(2024)
Article
Acoustics
Nicco Ulbricht, Alain Boldini, Peng Zhang, Maurizio Porfiri
Summary: The quantification of fluid-structure interactions in marine structures is crucial for their design and optimization. In this study, an analytical solution for the free vibration of a bidirectional composite in contact with a fluid is proposed. By imposing continuity conditions and boundary conditions, the coupled fluid-structure problem is solved and applied to sandwich structures in naval construction, offering insights into the effects of water on mode shapes and through-the-thickness profiles of displacement and stress.
JOURNAL OF SOUND AND VIBRATION
(2024)
Article
Acoustics
Shahram Hadian Jazi, Mostafa Hadian, Keivan Torabi
Summary: Non-uniformity and damage are the main focus in studying vibrations of beam elements. An exact closed-form explicit solution for the transverse displacement of a nonuniform multi-cracked beam is introduced using generalized functions and distributional derivative concepts. By introducing non-dimensional parameters, the motion equation and its closed-form solution are obtained based on four fundamental functions. The impact of crack count, location, intensity, and boundary conditions on natural frequency and mode shape is evaluated through numerical study.
JOURNAL OF SOUND AND VIBRATION
(2024)
Article
Acoustics
Eugenio Tramacere, Marius Pakstys, Renato Galluzzi, Nicola Amati, Andrea Tonoli, Torbjoern A. Lembke
Summary: This paper proposes the experimental stabilization of electrodynamic maglev systems by means of passive components, providing key technological support for the Hyperloop concept of high-speed and sustainable transportation.
JOURNAL OF SOUND AND VIBRATION
(2024)
Article
Acoustics
Pengfei Deng, Xing Tan, He Li
Summary: In this paper, the authors improve the surface morphology method and study the bit-rock interaction model between the rock and the PDC bit, taking into account the impact of blade shape and cutter arrangement. They establish a dynamic model for a deep drilling system equipped with an arbitrary shape PDC bit and propose a stability prediction method. The results show that the shape of the blades and arrangement of the cutters on the PDC bit significantly affect the nonlinear vibration of the drilling system.
JOURNAL OF SOUND AND VIBRATION
(2024)
Article
Acoustics
Salvador Rodriguez-Blanco, Javier Gonzalez-Monge, Carlos Martel
Summary: In modern LPT designs, the simultaneous presence of forced response and flutter in different operation regimes is unavoidable. Recent evidence suggests that the traditional linear superposition method may be overly conservative. This study examines the flutter and forced response interaction in a realistic low pressure turbine rotor and confirms that the actual response is much smaller than that predicted by linear superposition.
JOURNAL OF SOUND AND VIBRATION
(2024)
Article
Acoustics
Kabilan Baskaran, Nur Syafiqah Jamaluddin, Alper Celik, Djamel Rezgui, Mahdi Azarpeyvand
Summary: This study investigates the impact of the number of blades on the aeroacoustic characteristics and aerodynamic performance of propellers used in urban air mobility vehicles. The results show that different blade numbers exhibit distinct noise levels, providing valuable insights for further research on propeller noise and aerodynamic performance.
JOURNAL OF SOUND AND VIBRATION
(2024)
Article
Acoustics
Yongbo Peng, Peifang Sun
Summary: This study focuses on the reliability-based design optimization (RBDO) of the tuned mass-damper-inerter (TMDI) system under non-stationary excitations. The performance of the optimized TMDI system is evaluated using probability density evolution analysis. The results demonstrate the technical advantages of TMDI, including high vibration mitigation performance, considerable mass reduction, and less stroke demand.
JOURNAL OF SOUND AND VIBRATION
(2024)
Article
Acoustics
Guanfu Lin, Zhong-Rong Lu, Jike Liu, Li Wang
Summary: Vision-based measurement is an emerging method that enables full-field measurement with non-contact and high spatial resolution capabilities. This paper presents a single-camera method for measuring out-of-plane vibration of plate structures using motion-parametric homography to capture image variation and displacement response.
JOURNAL OF SOUND AND VIBRATION
(2024)
Article
Acoustics
Bronislaw Czaplewski, Mateusz Bocian, John H. G. Macdonald
Summary: Despite two decades of study, there is currently no model that can quantitatively explain pedestrian-generated lateral forces. This research proposes a foot placement control law based on empirical data to calibrate and generalize the rigid-leg inverted pendulum model (IPM) for predicting lateral structural stability.
JOURNAL OF SOUND AND VIBRATION
(2024)
Article
Acoustics
Justine Carpentier, Jean-Hugh Thomas, Charles Pezerat
Summary: This paper proposes an improved method for the identification of vibration sources on a car window using the corrected force analysis technique. By redefining inverse methods in polar coordinates, more accurate results can be obtained.
JOURNAL OF SOUND AND VIBRATION
(2024)
