Article
Engineering, Multidisciplinary
Chao Qun, Gao HaoHan, Tao JianFeng, Wang YuanHang, Zhou Jian, Liu ChengLiang
Summary: This study proposed a multi-sensor fusion method to improve fault diagnosis performance of axial piston pumps, using a convolutional neural network to combine three channels of vibration data for more accurate classification of faults. Experimental results showed that the method significantly enhanced the classification accuracy by adjusting the probability distribution according to the learned weight matrix.
SCIENCE CHINA-TECHNOLOGICAL SCIENCES
(2022)
Article
Engineering, Mechanical
Qun Chao, Haohan Gao, Jianfeng Tao, Chengliang Liu, Yuanhang Wang, Jian Zhou
Summary: This paper presents an end-to-end multi-sensor data fusion method for the fault diagnosis of axial piston pumps. By fusing vibration and pressure signals into RGB images and recognizing them using a convolutional neural network, the proposed method greatly improves the accuracy and robustness of fault diagnosis.
FRONTIERS OF MECHANICAL ENGINEERING
(2022)
Article
Chemistry, Physical
Shiqi Xia, Yimin Xia, Jiawei Xiang
Summary: Piston wear is a major failure mode of axial piston pumps. This paper proposes a vibration signal-based method for detecting piston wear and integrates feature selection into the learning process. Through the analysis and validation of 40 features, the importance of sparse features in piston wear detection is demonstrated.
Article
Chemistry, Multidisciplinary
Pawel Fic, Adam Czornik, Piotr Rosikowski
Summary: This article presents and compares different methods for predicting vibration velocity in axial piston pumps using neural networks. The research result shows that the developed models can clearly indicate whether the pump has malfunctioned or not across various operating points and working conditions, and the application of these models is reasonable in terms of performance quality and costs.
APPLIED SCIENCES-BASEL
(2023)
Article
Engineering, Industrial
Zhiying Wang, Zheng Zhou, Wengang Xu, Chuang Sun, Ruqiang Yan
Summary: This study proposes a physics informed neural network method for identifying fault severity in axial piston pumps. By combining physics models and neural network models, the method provides explicit physical interpretation to the identified results.
JOURNAL OF MANUFACTURING SYSTEMS
(2023)
Article
Acoustics
Hao Pang, Defa Wu, Yipan Deng, Qian Cheng, Yinshui Liu
Summary: This study investigates the effect of working medium on the noise and vibration of water hydraulic axial piston pump (WHAPP) through experimental and theory simulation methods. Results show that using oil as working medium leads to larger noise and vibration compared to using water, mainly due to the greater flow and pressure pulsation of oil. The differences in flow and pressure pulsation are primarily caused by the bulk modulus of the working medium.
Article
Engineering, Mechanical
Thomas Ransegnola, Lizhi Shang, Andrea Vacca
Summary: This paper utilizes a simulation model to study the rotary motion of the piston and slipper in swashplate type axial piston machines and explores the effects of operating parameters and ball socket friction on the machine behavior.
TRIBOLOGY INTERNATIONAL
(2022)
Article
Engineering, Mechanical
Pingting Ying, Hesheng Tang, Shaogan Ye, Yan Ren, Jiawei Xiang, Anil Kumar
Summary: In this study, an improved time-varying displacement excitation model considering the effects of defect sizes and bias angle is proposed to analyze the vibrations of a swashplate with a local defect. The dynamic model is validated through a test rig and can accurately predict the acceleration pulse waveform caused by the swashplate. The study provides a new method for dynamic simulation of a swashplate with a localized defect in an axial piston pump.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Multidisciplinary
Chaoang Xiao, Hesheng Tang, Yan Ren, Anil Kumar
Summary: This study introduces a fuzzy entropy assisted singular spectrum decomposition denoising method for extracting fault features in axial piston pump bearings. The method effectively reduces interference noise through two rounds of screening and demonstrates good application performance with practical engineering signals.
ALEXANDRIA ENGINEERING JOURNAL
(2022)
Article
Engineering, Multidisciplinary
You He, Hesheng Tang, Yan Ren, Anil Kumar
Summary: This research proposes an intelligent fault diagnosis method for axial piston pumps based on deep convolutional generative adversarial network (DCGAN). The method enhances fault features and expands dataset, extracts deep features using DCGAN and semi-supervised GAN (SGAN), and classifies the extracted features using a clustering algorithm to achieve fault diagnosis of the axial piston pump bearing. The experimental results show high diagnostic accuracy, superior generalization ability, and excellent anti-noise ability when evaluation indicators of the clustering results are close to 1.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2021)
Article
Mathematics
Shiqi Xia, Yimin Xia, Jiawei Xiang
Summary: This paper proposes a discharge pressure-based model and fault detection methodology for specific cavitation damage in axial piston pumps. The study investigates the effects of cavitation damage on discharge pressure and presents a method for detecting this damage.
Article
Engineering, Mechanical
Qiang Gao, Jiawei Xiang, Shumin Hou, Hesheng Tang, Yongteng Zhong, Shaogan Ye
Summary: An axial piston pump is a critical component in hydraulic systems with important fault detection requirements. A novel hybrid method combining L-kurtosis and enhanced clustering-based segmentation effectively recognizes and extracts faults in axial piston pumps. This method shows promising results in effectively handling faulty signals and improving fault diagnosis accuracy.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Automation & Control Systems
Chaoang Xiao, Hesheng Tang, Yan Ren, Jiawei Xiang, Anil Kumar
Summary: A hybrid method of MOMEDA and TEO is proposed to extract periodic impulses in axial piston pump bearings. The deconvolution periods and filter length are determined using Kurtosis and an advance-retreat algorithm. MOMEDA is used to enhance the periodic impulses, and TEO demodulation is employed to obtain fault frequencies.
Article
Engineering, Electrical & Electronic
Dong-Ning Chen, Zi-Yu Zhou, Dong-Bo Hu, Wen-Ping Liu, Ji-Tao Liu, Ya-Nan Chen
Summary: In this study, a fault diagnosis method of multisensor information fusion for axial piston pumps is proposed based on improved wavelet packet decomposition (WPD) and kernelized support tensor train machine (KSTTM). The experimental results show that multisensor information fusion can obtain better results compared with a single sensor, and the tensor-based method is robust to small sample size problems.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Multidisciplinary
You He, Hesheng Tang, Yan Ren, Anil Kumar
Summary: A deep multi-signal fusion adversarial model based transfer learning method is proposed to address the diverse working conditions of axial piston pumps in fault diagnosis. The method improves performance through a multi-signal fusion module and embedding a residual network.
Article
Engineering, Electrical & Electronic
Qiang Gao, Jun Young Jeon, Jiawei Xiang, Gyuhae Park
Summary: This article presents a non-equidistant T-shaped sensor cluster for localizing the impact on structures. The normal T-shaped sensor cluster configuration effectively removes blind areas by combining equidistant linear sensor array beamforming and an L-shaped sensor cluster method. However, the equidistant linear sensor array beamforming suffers from spatial aliasing effect, resulting in low localization accuracy. To address this, the proposed non-equidistant T-shaped sensor cluster utilizes nonequidistant linear sensor array beamforming method to improve impact localization accuracy in the regions near vertical directions.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Yizhu Shi, Yuqing Zhou, Yan Ren, Weifang Sun, Jiawei Xiang
Summary: This research proposes a hybrid method for identifying the spring energy storage state in circuit breaker operating mechanisms. The method utilizes the Gramian angular field (GAF) to represent the dynamic characteristics evolution process and combines it with a convolutional block attention module (CBAM) and residual network (ResNet). Experimental results demonstrate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Industrial
Hui Wang, Junkang Zheng, Jiawei Xiang
Summary: Digital twin is the embodiment of the most advanced achievements of the current simulation technology theory development and the direction of intelligent development in the future. However, it is a great challenge to really integrate it into practical project application. Motivated by DT, an application method combining numerical simulation model and machine learning classification is proposed to show the advantages of digital twin.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Industrial
Yi Liu, Hang Xiang, Zhansi Jiang, Jiawei Xiang
Summary: Intelligent fault diagnosis methods are effective in ensuring the safety and reliability of key parts of rotating machinery. However, the lack of data during equipment acceptance period and the assumption of high data quality affect the reliability of results. To address these issues, a time-frequency-based method is introduced to analyze impulse components based on fault features. An accurate time-frequency analysis method named the second-order transient-extracting S-transform is proposed to overcome the influence of uncertain parameters. It produces a highly concentrated time-frequency representation and demonstrates higher accuracy in feature detection compared to other methods.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Chemistry, Analytical
Qizhe Lin, Xiaoqi Li, Bicheng Tu, Junwei Cao, Ming Zhang, Jiawei Xiang
Summary: This study proposes a two-stage method for estimating the SOC of a lithium-ion battery by combining a second-order resistor-capacitor (RC) equivalent circuit model and an eXogenous Kalman filter (XKF). The SOC estimation values obtained by a stable observer are fed into XKF to enhance accuracy and stability. Experimental results show that the proposed method outperforms the commonly used EKF method in SOC estimation.
Article
Engineering, Marine
Hu Jiang, Yongying Jiang, Jiawei Xiang
Summary: Structural damage detection based on vibration signals has gained attention for its easy implementation. However, this method is not directly applicable to structures in operation. To address this limitation, a two-stage multi-damage detection method using operating deflection shape (ODS) and random forest (RF) techniques has been developed. It utilizes 2D wavelet transform and finite element method (FEM) to identify damage locations and establish relationships between natural frequencies and damage severities. Numerical simulations and experimental tests show that this method achieves acceptable damage-detection precision.
Article
Chemistry, Analytical
Yi Liu, Hang Xiang, Zhansi Jiang, Jiawei Xiang
Summary: Intelligent fault diagnosis of roller bearings faces challenges from similar distribution of train and test datasets and limitations in installation positions of accelerometer sensors in industrial environments. This paper proposes a DA-ResNet model using MMD and a residual connection for cross-domain diagnosis of roller bearings based on acoustic and vibration data. Experimental cases verify the necessity of multi-source data and improvement in recognition accuracy through transfer learning.
Article
Engineering, Civil
Yongying Jiang, Jinshang Sun, Qizhe Lin, Jiawei Xiang
Summary: A two-stage method is proposed to effectively identify damages in aluminum plates. The first stage detects damage locations by subtracting the curve modal fitting shape from the curvature modal shape. In the second stage, particle swarm optimization is used to search for the damage severities from a pre-calculated damage evaluation database.
THIN-WALLED STRUCTURES
(2023)
Article
Engineering, Electrical & Electronic
Jiahao Li, Yi Liu, Jiawei Xiang
Summary: Maximum CYCBD is a blind deconvolution method used to extract faults in mechanical systems. It faces challenges of setting the cyclic frequency in advance and defining a suitable filter length. To address these issues, an optimal maximum CYCBD method is developed. It involves processing raw signals, estimating the cyclic frequency, and determining the filter length adaptively. The proposed method is validated with data from a bearing dataset and experimental test rigs.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Xiao Zhuang, Di Zhou, Jing Cai, Hongfu Zuo, Xufeng Zhao, Jiawei Xiang
Summary: In this study, a novel image processing and deep normalized convolutional neural network (DNCNN) for location measurement and reading distance prediction of RFID multitags are proposed. An experimental platform is designed involving image technology to measure the reading distance and collect the images of RFID multitags. The proposed DNCNN effectively predicts the reading distance of RFID multitags by deeply mining the relationship between the tags' location distributions and corresponding reading distance.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Multidisciplinary
Qi Wei, Jiawei Xiang, Weiping Zhu, Hongjiu Hu
Summary: A novel method combining WBEM with frequency-independent fundamental solutions is proposed to determine the band structures of fluid-solid PCs. Integral equations are based on frequency-independent fundamental solutions, avoiding nonlinear eigenvalue problems and reducing computing time. Domain integral terms are handled with RIM and DRM. B-spline wavelet and wavelet coefficients are used to approximate the physical boundary conditions. Coupling conditions and Bloch theorem are applied to wavelet coefficients. Matrix compression technique is used to truncate small matrix entries generated by wavelet vanishing moment characteristics. Examples are provided to demonstrate the accuracy and efficiency of the method.
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING
(2023)
Article
Engineering, Multidisciplinary
Hu Jiang, Yongying Jiang, Jiawei Xiang
Summary: Renewable energy has seen significant growth in recent years, leading to increased maintenance of equipment. Structural health monitoring techniques can help reduce maintenance costs, especially for wind turbine blade damages. However, most current methods are not suitable for online measurements and quantitative detections. This study develops a quantitative damage detection method that can identify multiple damages in wind turbine blades under normal operating conditions. The method is shown to be effective in detecting multiple damages with a certain level of noise robustness.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Yuqing Zhou, Hongche Wang, Gonghai Wang, Anil Kumar, Weifang Sun, Jiawei Xiang
Summary: In recent years, deep learning-based methods have made remarkable achievements in the intelligent fault diagnosis of rotating machinery. However, the lack of labeled and large unlabeled samples in actual industrial scenes affects the performance of supervised learning methods. This paper proposes a novel semi-supervised fault diagnosis method based on multiscale permutation entropy (MPE) enhanced contrastive learning (CL). Experimental results in gearbox and milling tool fault diagnosis experiments show that the proposed MPE-CL method outperforms other benchmark methods with classification accuracy above 95.4% and 96.0% when the labeled training dataset size is 50/class, respectively.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Electrical & Electronic
Yi Liu, Hang Xiang, Zhansi Jiang, Jiawei Xiang
Summary: An improved general linear chirplet transform method is developed to overcome the limitations of traditional short-time Fourier transform (STFT)-based methods in processing non-stationary signals. By iteratively upgrading the instantaneous frequency (IF) and introducing a synchrosqueezing operator, the method improves the estimation accuracy of IF curves and enhances the concentration of time-frequency representation under variable operating conditions. Experiments with simulated data confirm the effectiveness of the enhanced time-frequency analysis (TFA) method, showing superior sharpness of IF curves and enhanced time-frequency readability compared to other advanced TFA methods, as well as superior feature extraction ability.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Chemistry, Analytical
Hanlin Guan, Ren Yan, Hesheng Tang, Jiawei Xiang
Summary: This paper proposes an effective intelligent fault diagnosis method for hydraulic multi-way valves using an improved Squeeze-Excitation Convolution Neural Network and Gated Recurrent Unit (SECNN-GRU). The method extracts statistical and fault features, and combines convolutional neural network and recurrent neural network for feature extraction and fusion, achieving high accuracy fault diagnosis.