Journal of Dynamics, Monitoring and Diagnostics最新文献_第2页

Investigation into Impedance Measurements for Rapid Capacity Estimation of Lithium-ion Batteries in Electric Vehicles 用于快速估算电动汽车锂离子电池容量的阻抗测量研究

Journal of Dynamics, Monitoring and Diagnostics Pub Date : 2024-01-08 DOI: 10.37965/jdmd.2024.475

Xiaoyu Zhao, Z. Wang, Eric Li, Haiyan Miao

{"title":"Investigation into Impedance Measurements for Rapid Capacity Estimation of Lithium-ion Batteries in Electric Vehicles","authors":"Xiaoyu Zhao, Z. Wang, Eric Li, Haiyan Miao","doi":"10.37965/jdmd.2024.475","DOIUrl":"https://doi.org/10.37965/jdmd.2024.475","url":null,"abstract":"With the dramatic increase in electric vehicles (EVs) globally, the demand for lithium-ion batteries has grown dramatically, resulting in many batteries being retired in the future. Developing a rapid and robust capacity estimation method is a challenging work to recognize the battery ageing level on service and provide regroup strategy of the retied batteries in secondary use. There are still limitations on the current rapid battery capacity estimation methods, such as direct current internal resistance (DCIR) and electrochemical impedance spectroscopy (EIS), in terms of efficiency and robustness. To address the challenges, this paper proposes an improved version of DCIR, named pulse impedance technique (PIT), for rapid battery capacity estimation with more robustness. First, PIT is carried out based on the transient current excitation and dynamic voltage measurement using the high sampling frequency, in which the coherence analysis is used to guide the selection of a reliable frequency band. The battery impedance can be extracted in a wide range of frequency bands compared to the traditional DCIR method, which obtains more information on the battery capacity evaluation. Second, various statistical variables are used to extract ageing features and Pearson correlation analysis is applied to determine the highly correlated features. Then a linear regression model is developed to map the relationship between extracted features and battery capacity. To validate the performance of the proposed method, the experimental system is designed to conduct comparative studies between PIT and EIS based on the two 18650 batteries connected in series. The results reveal that the proposed PIT can provide comparative indicators to EIS, which contributes higher estimation accuracy of the proposed PIT method than EIS technology with lower time and cost.\u0000 \u0000Conflict of Interest Statement\u0000The authors declare no conflicts of interest.","PeriodicalId":136221,"journal":{"name":"Journal of Dynamics, Monitoring and Diagnostics","volume":"23 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139445479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Bearing Fault Diagnosis Based on Graph Formulation and Graph Convolutional Network 基于图表述和图卷积网络的轴承故障诊断

Journal of Dynamics, Monitoring and Diagnostics Pub Date : 2023-12-08 DOI: 10.37965/jdmd.2023.468

Xin Wang, Wenjin Zhou, Xiao-Dong Li

{"title":"Bearing Fault Diagnosis Based on Graph Formulation and Graph Convolutional Network","authors":"Xin Wang, Wenjin Zhou, Xiao-Dong Li","doi":"10.37965/jdmd.2023.468","DOIUrl":"https://doi.org/10.37965/jdmd.2023.468","url":null,"abstract":"Bearing fault diagnosis stands as a critical component in the maintenance of rotating machinery. Many prevalent deep learning techniques are tailored to Euclidean datasets such as audio, image, and video. However, these methods falter when confronting non-Euclidean datasets, notably graph representations. In response, here we introduce an innovative approach harnessing the Graph Convolutional Network (GCN) to analyze graph data derived from vibration signals related to bearing faults. This enhances the precision and reliability of fault diagnosis. Our methodology initiates by deriving a periodogram from the unprocessed vibration signals. Subsequently, this periodogram is mapped into a graph format, upon which the GCN is engaged for classification purposes. We substantiate the efficacy of our approach through rigorous experimental assessments conducted on a collection of ten bearing sets. Within these experiments, an accelerometer chronicles vibration signals across varying load conditions. We probe into the diagnostic accuracy rates across diverse loads and Signal-to-Noise Ratios (SNRs). Furthermore, a comparative evaluation of our method against several established algorithms delineated in this study is undertaken. Empirical observations confirm that our GCN-based strategy registers an elevated diagnostic accuracy quotient.","PeriodicalId":136221,"journal":{"name":"Journal of Dynamics, Monitoring and Diagnostics","volume":"52 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138588134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Review on Dynamic Modeling and Vibration Characteristics of Rotating Cracked Blades 旋转裂纹叶片的动态建模和振动特性综述

Journal of Dynamics, Monitoring and Diagnostics Pub Date : 2023-11-28 DOI: 10.37965/jdmd.2023.465

Hui Ma, Z. Wu, J. Zeng, Weiwei Wang, Hongji Wang, Hong Guan, Wenming Zhang

引用次数: 0

An Improved Second-Order Multi-Synchrosqueezing Transform for the Analysis of Non-Stationary Signals 一种用于非平稳信号分析的改进二阶多同步压缩变换

Journal of Dynamics, Monitoring and Diagnostics Pub Date : 2023-08-15 DOI: 10.37965/jdmd.2023.207

Kewen Wang, Yajun Shang, Yongzheng Lu, Tianran Lin

{"title":"An Improved Second-Order Multi-Synchrosqueezing Transform for the Analysis of Non-Stationary Signals","authors":"Kewen Wang, Yajun Shang, Yongzheng Lu, Tianran Lin","doi":"10.37965/jdmd.2023.207","DOIUrl":"https://doi.org/10.37965/jdmd.2023.207","url":null,"abstract":"Second-order multi-synchrosqueezing transform (SMSST), an effective tool for the analysis of non-stationary signals, can significantly improve the time-frequency resolution of a non-stationary signal. Though, the noise energy in the signal can also be enhanced in the transform which can largely affect the characteristic frequency component identification for an accurate fault diagnostic. An improved algorithm termed as an improved second-order multi-synchrosqueezing transform (ISMSST) is then proposed in this study to alleviate the problem of noise interference in the analysis of non-stationary signals. In the study, the time-frequency (TF) distribution of a non-stationary signal is calculated first using SMSST, and then a δ function is constructed based on a newly proposed time-frequency operator (TFO) which is then substituted back into SMSST to produce a noise-free time frequency result. The effectiveness of the technique is validated by comparing the TF results obtained using the proposed algorithm and those using other TFA techniques in the analysis of a simulated signal and an experimental data. The result shows that the current technique can render the most accurate TFA result within the TFA techniques employed in this study.","PeriodicalId":136221,"journal":{"name":"Journal of Dynamics, Monitoring and Diagnostics","volume":"735 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131508315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Electric Vehicles Lithium-Polymer Ion Battery Dynamic Behaviour Charging Identification and Modelling Scheme 电动汽车锂聚合物离子电池充电动态行为识别与建模方案

Journal of Dynamics, Monitoring and Diagnostics Pub Date : 2023-08-03 DOI: 10.37965/jdmd.2023.398

P. Makeen, Hani A. Ghali, S. Memon, F. Duan

{"title":"Electric Vehicles Lithium-Polymer Ion Battery Dynamic Behaviour Charging Identification and Modelling Scheme","authors":"P. Makeen, Hani A. Ghali, S. Memon, F. Duan","doi":"10.37965/jdmd.2023.398","DOIUrl":"https://doi.org/10.37965/jdmd.2023.398","url":null,"abstract":"Lithium-ion batteries are considered the substantial electrical storage element for electric vehicles (EVs). The battery model is the basis of battery monitoring, efficient charging, and safety management. Non-linear modelling is the key to representing the battery and its dynamic internal parameters and performance. This paper proposes a smart scheme to model the lithium-polymer ion battery while monitoring its present charging current and terminal voltage at various ambient conditions (temperature and relative humidity). Firstly, the suggested framework investigated the impact of temperature and relative humidity on the charging process using the constant current-constant voltage (CC-CV) charging protocol. This will be followed by monitoring the battery at the surrounding operating temperature and relative humidity. Hence, efficient non-linear modelling of the EV battery dynamic behaviour using the Hammerstein-Wiener (H-W) model is implemented. The H-W model is considered a black box model that can represent the battery without any mathematical equivalent circuit model which reduces the computation complexity. Finally, the model beholds the boundaries of the charging process that not affecting on the lifetime of the battery. Several dynamic models are applied and tested experimentally to ensure the effectiveness of the proposed scheme under various ambient conditions where the temperature is fixed at 40°C and the relative humidity (RH) at 35%, 52%, and 70%. The best fit using the H-W model reached 91.83% to describe the dynamic behaviour of the battery with a maximum percentage of error 0.1V which is in good agreement with the literature survey. Besides, the model has been scaled up to represent a real EV and expressed the significance of the proposed H-W model.","PeriodicalId":136221,"journal":{"name":"Journal of Dynamics, Monitoring and Diagnostics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128543053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Inter-shaft Bearing Fault Diagnosis Based on Aero-engine System: A Benchmarking Dataset Study 基于航空发动机轴间轴承故障诊断的基准数据集研究

Journal of Dynamics, Monitoring and Diagnostics Pub Date : 2023-08-03 DOI: 10.37965/jdmd.2023.314

L. Hou, H. Yi, Yuhong Jin, Min Gui, Lianzheng Sui, Jianwei Zhang, Yu-shu Chen

{"title":"Inter-shaft Bearing Fault Diagnosis Based on Aero-engine System: A Benchmarking Dataset Study","authors":"L. Hou, H. Yi, Yuhong Jin, Min Gui, Lianzheng Sui, Jianwei Zhang, Yu-shu Chen","doi":"10.37965/jdmd.2023.314","DOIUrl":"https://doi.org/10.37965/jdmd.2023.314","url":null,"abstract":"In this paper, the aero-engine test with inter-shaft bearing fault is carried out, and a dataset is proposed for the first time based on the vibration signal of rotors and casings. First, a test rig based on a real aero-engine is established, driven by motors and equipped with a lubricating system. Then, the aero-engine is disassembled and assembled following the specification process, and the inter-shaft bearing with artificial fault is replaced. Next, the aero-engine test is conducted at 28 groups of high and low pressure speeds. Six measuring points are arranged, including two displacement sensors to test the displacement vibration signals of the low pressure rotor and four acceleration sensors to test the acceleration vibration signals of the casing. The test results are integrated into an inter-shaft bearing fault dataset. Finally, based on the dataset in this paper, frequency spectrum, envelope spectrum, CNN, LSTM and TST are used for fault diagnosis, and the results are compared with those of CWRU and XJTU datasets. The results show that the characteristic fault frequency cannot be found directly in the spectrum and envelope spectrum corresponding to this paper's dataset but in CWRU and XJTU datasets. Using CNN, LSTM and TST for fault diagnosis of the dataset in this paper, the accuracy is 83.13%, 85.41% and 71.07%, respectively, much lower than the diagnosis results of CWRU and XJTU datasets. It can be seen that the dataset in this paper is closer to the actual fault diagnosis situation and is a more challenging dataset. This dataset provides a new benchmark for the validation of fault diagnosis methods. Mendeley data: https://github.com/HouLeiHIT/HIT-dataset.","PeriodicalId":136221,"journal":{"name":"Journal of Dynamics, Monitoring and Diagnostics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125883964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Improved Coupled Dynamic Modeling for Exploring Gearbox Vibrations Considering Local Defects 考虑局部缺陷的齿轮箱振动改进耦合动力学建模

Journal of Dynamics, Monitoring and Diagnostics Pub Date : 2023-07-26 DOI: 10.37965/jdmd.2023.186

Y. Han, Xiaohui Chen, Jiawei Xiao, J. Gu, Minmin Xu

{"title":"An Improved Coupled Dynamic Modeling for Exploring Gearbox Vibrations Considering Local Defects","authors":"Y. Han, Xiaohui Chen, Jiawei Xiao, J. Gu, Minmin Xu","doi":"10.37965/jdmd.2023.186","DOIUrl":"https://doi.org/10.37965/jdmd.2023.186","url":null,"abstract":"Gearbox is a key part in machinery, in which gear, shaft and bearing operate together to transmit motion and power. The wide usage and high failure rate of gearbox make it attract much attention on its health monitoring and fault diagnosis. Dynamic modeling can study the mechanism under different faults and provide theoretical foundation for fault detection. However, current commonly used gear dynamic model usually neglects the influence of bearing and shaft, resulting in incomplete understanding on gearbox fault diagnosis especially under the effect of local defects on gear and shaft. To address this problem, an improved gear-shaft-bearing-housing dynamic model is proposed to reveal the vibration mechanism and responses considering shaft whirling and gear local defects. Firstly, an eighteen degree-of-freedom gearbox dynamic model is proposed, taking into account the interaction among gear, bearing and shaft. Secondly, the dynamic model is iteratively solved. Then, vibration responses are expounded and analyzed considering gear spalling and shaft crack. Numerical results show that the gear mesh frequency and its harmonics have higher amplitude through the spectrum. Vibration RMS and the shaft rotating frequency increase with the spalling size and shaft crack angle in general. An experiment is designed to verify the rationality of the proposed gearbox model. Lastly, comprehensive analysis under different spalling size and shaft crack angle are analyzed. Results show that when spalling size and crack angle is lager, RMS and the amplitude of shaft rotating frequency will not increase linearly. The dynamic model can accurately simulate the vibration of gear transmission system, which is helpful to gearbox fault diagnosis.","PeriodicalId":136221,"journal":{"name":"Journal of Dynamics, Monitoring and Diagnostics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130436444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Wind Turbine Planetary Gearbox Fault Diagnosis via Proportion-Extracting Synchrosqueezing Chirplet Transform 基于比例提取同步压缩小波变换的风电行星齿轮箱故障诊断

Journal of Dynamics, Monitoring and Diagnostics Pub Date : 2023-07-12 DOI: 10.37965/jdmd.2023.151

Dong Zhang, Zhipeng Feng

{"title":"Wind Turbine Planetary Gearbox Fault Diagnosis via Proportion-Extracting Synchrosqueezing Chirplet Transform","authors":"Dong Zhang, Zhipeng Feng","doi":"10.37965/jdmd.2023.151","DOIUrl":"https://doi.org/10.37965/jdmd.2023.151","url":null,"abstract":"Wind turbine planetary gearboxes usually work under time-varying conditions, leading to nonstationary vibration signals. These signals often consist of multiple time-varying components with close instantaneous frequencies. Therefore, high-quality time-frequency analysis (TFA) is needed to extract the time-frequency feature from such nonstationary signals for fault diagnosis. However, it is difficult to obtain high-quality time-frequency representations (TFRs) through conventional TFA methods due to low resolution and time-frequency blurs. To address this issue, we propose a new TFA method termed the proportion-extracting synchrosqueezing chirplet transform (PESCT). Firstly, the proportion-extracting chirplet transform (PECT) is employed to generate high-resolution underlying TFRs. Then, the energy concentration of the underlying TFRs is enhanced via the synchrosqueezing transform. Finally, wind turbine planetary gearbox fault can be diagnosed by analysis of the dominant time-varying components revealed by the concentrated TFRs with high resolution. The proposed PESCT is suitable for achieving high-quality TFRs for complicated nonstationary signals. Numerical and experimental analyses validate the effectiveness of the PESCT in characterizing the nonstationary signals from wind turbine planetary gearboxes.","PeriodicalId":136221,"journal":{"name":"Journal of Dynamics, Monitoring and Diagnostics","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114803479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

New Sensing Technologies for Monitoring Machinery, Structures, and Manufacturing Processes 监测机械、结构和制造过程的新传感技术

Journal of Dynamics, Monitoring and Diagnostics Pub Date : 2023-06-08 DOI: 10.37965/jdmd.2023.322

JDMD Editorial Office, Zhaoyan Fan, R. Gao, Qingbo He, Yi Huang, Tianxi Jiang, Zhike Peng, L. Thévenaz, Yuyong Xiong, Shuncong Zhong

引用次数: 1

Rolling Bearing Fault Diagnosis Based On Convolutional Capsule Network 基于卷积胶囊网络的滚动轴承故障诊断

Journal of Dynamics, Monitoring and Diagnostics Pub Date : 2023-06-06 DOI: 10.37965/jdmd.2023.260

G. Jiang, Decai Li, Ke Feng, Yongbo Li, Jinde Zheng, Qing Ni, He Li

{"title":"Rolling Bearing Fault Diagnosis Based On Convolutional Capsule Network","authors":"G. Jiang, Decai Li, Ke Feng, Yongbo Li, Jinde Zheng, Qing Ni, He Li","doi":"10.37965/jdmd.2023.260","DOIUrl":"https://doi.org/10.37965/jdmd.2023.260","url":null,"abstract":"Fault diagnosis technology has been widely applied and is an important part of ensuring the safe operation of mechanical equipment. In response to the problem of frequent faults in rolling bearings, this paper designs a rolling bearing fault diagnosis method based on Convolutional Capsule Network (CCN). More specifically, the original vibration signal is converted into a two-dimensional time-frequency image using continuous wavelet transform (CWT), and the feature extraction is performed on the two-dimensional time-frequency image using the convolution layer at the front end of the network, and the extracted features are input into the capsule network. The capsule network converts the extracted features into vector neurons, and the dynamic routing algorithm is used to achieve feature transfer and output the results of fault diagnosis. Two different datasets are used to compare with other traditional deep learning models to verify the fault diagnosis capability of the method. The results show that the convolutional capsule network has good diagnostic capability under different working conditions, even in the presence of noise and insufficient samples, compared to other models. This method contributes to the safe and reliable operation of mechanical equipment and is suitable for other rotating scenarios.","PeriodicalId":136221,"journal":{"name":"Journal of Dynamics, Monitoring and Diagnostics","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122626954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0