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Prof. Lijun Zhang
University of Science and Technology Beijing

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Research Keywords & Expertise

0 Artificial Intelligence
0 Big Data
0 Lithium-ion Batteries
0 prognostic and health management (PHM)
0 digital twins

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Journal article
Published: 23 July 2020 in World Electric Vehicle Journal
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There are different types of rechargeable batteries, but lithium-ion battery has proven to be superior due to its features including small size, more volumetric energy density, longer life, and low maintenance. However, lithium-ion batteries face safety issues as one of the common challenges in their development, necessitating research in this area. For the safe operation of lithium-ion batteries, state estimation is very significant and battery parameter identification is the core in battery state estimation. The battery management system for electric vehicle application must perform a few estimation tasks in real-time. Battery state estimation is defined by the battery model adopted and its accuracy impacts the accuracy of state estimation. The knowledge of the actual operating conditions of electric vehicles requires the application of an accurate battery model; for our research, we adopted the use of the dual extended Kalman filter and it demonstrated that it yields more accurate and robust state estimation results. Since no single battery model can satisfy all the requirements of battery estimation and parameter identification, the hybridization of battery models together with the introduction of internal sensors to batteries to measure battery internal reactions is very essential. Similarly, since the current battery models rarely consider the coupling effect of vibration and temperature dynamics on model parameters during state estimation, this research goal is to identify the battery parameters and then present the effect of the vibration and temperature dynamics in battery state estimation.

ACS Style

Zachary Bosire Omariba; Lijun Zhang; Hanwen Kang; Dongbai Sun. Parameter Identification and State Estimation of Lithium-Ion Batteries for Electric Vehicles with Vibration and Temperature Dynamics. World Electric Vehicle Journal 2020, 11, 50 .

AMA Style

Zachary Bosire Omariba, Lijun Zhang, Hanwen Kang, Dongbai Sun. Parameter Identification and State Estimation of Lithium-Ion Batteries for Electric Vehicles with Vibration and Temperature Dynamics. World Electric Vehicle Journal. 2020; 11 (3):50.

Chicago/Turabian Style

Zachary Bosire Omariba; Lijun Zhang; Hanwen Kang; Dongbai Sun. 2020. "Parameter Identification and State Estimation of Lithium-Ion Batteries for Electric Vehicles with Vibration and Temperature Dynamics." World Electric Vehicle Journal 11, no. 3: 50.

Preprint content
Published: 18 June 2020
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For fault failures of a steam turbine occur frequently and cause huge losses, it is important to identify the fault category. A steam turbine clustering fault diagnosis method based on t-distribution stochastic neighborhood embedding (t-SNE) and extreme gradient boosting (XGBoost) is proposed. Firstly, the t-SNE algorithm is used to map high-dimensional data to low-dimensional space, and data clustering is performed in low-dimensional space. Combined with the fault records of the power plant, the fault data and health data of the clustering result are distinguished. Then, the imbalance problem in the data is processed by the synthetic minority over-sampling technique (SMOTE) algorithm to obtain the steam turbine characteristic data set with fault labels. Finally, we used the XGBoost to solve this multiclassification problem. In the experiment, the method achieved the best performance with an overall accuracy of 97% and early warning at least two hours in advance. The experimental results show that this method can effectively evaluate the state and make fault warning for power plant equipment.

ACS Style

Xizhe Wang; Lijun Zhang. Novel Intelligent Method for Fault Diagnosis of Steam Turbine Based on T-SNE and XGBoost. 2020, 1 .

AMA Style

Xizhe Wang, Lijun Zhang. Novel Intelligent Method for Fault Diagnosis of Steam Turbine Based on T-SNE and XGBoost. . 2020; ():1.

Chicago/Turabian Style

Xizhe Wang; Lijun Zhang. 2020. "Novel Intelligent Method for Fault Diagnosis of Steam Turbine Based on T-SNE and XGBoost." , no. : 1.

Review
Published: 12 September 2019 in IEEE Access
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Batteries are gaining entry into every home and office for they are widely used because of their variant benefits. However, these batteries are prone to failure caused by charge imbalance in the batteries connected in either series or parallel, which can sometimes be catastrophic and hence they require to be properly monitored in a real-time manner. There exist many battery balancing schemes which are broadly grouped into either passive or active schemes. All these schemes have their own advantages and disadvantages, and hence it is upon the user to decide on which scheme will best work for them. However, research has proven that the hybrid scheme will be the best as it couples the benefits of all schemes. This study will review the various battery cell balancing methodologies and evaluate their relationship with battery performance. At present there are a few studies tackling the mechanical vibration of battery balancing performance. This study shows that battery balancing performance during long-term should be evaluated from various temperature and vibration frequencies.

ACS Style

Zachary Bosire Omariba; Lijun Zhang; Dongbai Sun. Review of Battery Cell Balancing Methodologies for Optimizing Battery Pack Performance in Electric Vehicles. IEEE Access 2019, 7, 129335 -129352.

AMA Style

Zachary Bosire Omariba, Lijun Zhang, Dongbai Sun. Review of Battery Cell Balancing Methodologies for Optimizing Battery Pack Performance in Electric Vehicles. IEEE Access. 2019; 7 (99):129335-129352.

Chicago/Turabian Style

Zachary Bosire Omariba; Lijun Zhang; Dongbai Sun. 2019. "Review of Battery Cell Balancing Methodologies for Optimizing Battery Pack Performance in Electric Vehicles." IEEE Access 7, no. 99: 129335-129352.

Journal article
Published: 22 October 2018 in Energies
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At present, a variety of standardized 18650 commercial cylindrical lithium-ion batteries are widely used in new energy automotive industries. In this paper, the Panasonic NCR18650PF cylindrical lithium-ion batteries were studied. The NEWWARE BTS4000 battery test platform is used to test the electrical performances under temperature, vibration and temperature-vibration coupling conditions. Under the temperature conditions, the discharge capacity of the same battery at the low temperature was only 85.9% of that at the high temperature. Under the vibration condition, mathematical statistics methods (the Wilcoxon Rank-Sum test and the Kruskal-Wallis test) were used to analyze changes of the battery capacity and the internal resistance. Changes at a confidence level of 95% in the capacity and the internal resistance were considered to be significantly different between the vibration conditions at 5 Hz, 10 Hz, 20 Hz and 30 Hz versus the non-vibration condition. The internal resistance of the battery under the Y-direction vibration was the largest, and the difference was significant. Under the temperature-vibration coupling conditions, the orthogonal table L9 (34) was designed. It was found out that three factors were arranged in order of temperature, vibration frequency and vibration direction. Among them, the temperature factor is the main influencing factor affecting the performance of lithium-ion batteries.

ACS Style

Lijun Zhang; Zhongqiang Mu; Xiangyu Gao. Coupling Analysis and Performance Study of Commercial 18650 Lithium-Ion Batteries under Conditions of Temperature and Vibration. Energies 2018, 11, 2856 .

AMA Style

Lijun Zhang, Zhongqiang Mu, Xiangyu Gao. Coupling Analysis and Performance Study of Commercial 18650 Lithium-Ion Batteries under Conditions of Temperature and Vibration. Energies. 2018; 11 (10):2856.

Chicago/Turabian Style

Lijun Zhang; Zhongqiang Mu; Xiangyu Gao. 2018. "Coupling Analysis and Performance Study of Commercial 18650 Lithium-Ion Batteries under Conditions of Temperature and Vibration." Energies 11, no. 10: 2856.

Journal article
Published: 25 September 2018 in Energies
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When wind turbine blades are icing, the output power of a wind turbine tends to reduce, thus informing the selection of two basic variables of wind speed and power. Then other features, such as the degree of power deviation from the power curve fitted by normal sample data, are extracted to build the model based on the random forest classifier with the confusion matrix for result assessment. The model indicates that it has high accuracy and good generalization ability verified with the data from the China Industrial Big Data Innovation Competition. This study looks at ice detection on wind turbine blades using supervisory control and data acquisition (SCADA) data and thereafter a model based on the random forest classifier is proposed. Compared with other classification models, the model based on the random forest classifier is more accurate and more efficient in terms of computing capabilities, making it more suitable for the practical application on ice detection.

ACS Style

Lijun Zhang; Kai Liu; Yufeng Wang; Zachary Bosire Omariba. Ice Detection Model of Wind Turbine Blades Based on Random Forest Classifier. Energies 2018, 11, 2548 .

AMA Style

Lijun Zhang, Kai Liu, Yufeng Wang, Zachary Bosire Omariba. Ice Detection Model of Wind Turbine Blades Based on Random Forest Classifier. Energies. 2018; 11 (10):2548.

Chicago/Turabian Style

Lijun Zhang; Kai Liu; Yufeng Wang; Zachary Bosire Omariba. 2018. "Ice Detection Model of Wind Turbine Blades Based on Random Forest Classifier." Energies 11, no. 10: 2548.

Journal article
Published: 30 July 2018 in Future Internet
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Researchers from different disciplines, such as materials science, computer science, safety science, mechanical engineering and controlling engineering, have aimed to improve the quality of manufacturing engineering processes. Considering the requirements of research and development of advanced materials, reliable manufacturing and collaborative innovation, a multidiscipline integrated platform framework based on probabilistic analysis for manufacturing engineering processes is proposed. The proposed platform consists of three logical layers: The requirement layer, the database layer and the application layer. The platform is intended to be a scalable system to gradually supplement related data, models and approaches. The main key technologies of the platform, encapsulation methods, information fusion approaches and the collaborative mechanism are also discussed. The proposed platform will also be gradually improved in the future. In order to exchange information for manufacturing engineering processes, scientists and engineers of different institutes of materials science and manufacturing engineering should strengthen their cooperation.

ACS Style

Lijun Zhang; Kai Liu; Jian Liu. Multidiscipline Integrated Platform Based on Probabilistic Analysis for Manufacturing Engineering Processes. Future Internet 2018, 10, 70 .

AMA Style

Lijun Zhang, Kai Liu, Jian Liu. Multidiscipline Integrated Platform Based on Probabilistic Analysis for Manufacturing Engineering Processes. Future Internet. 2018; 10 (8):70.

Chicago/Turabian Style

Lijun Zhang; Kai Liu; Jian Liu. 2018. "Multidiscipline Integrated Platform Based on Probabilistic Analysis for Manufacturing Engineering Processes." Future Internet 10, no. 8: 70.

Review
Published: 15 May 2018 in Electronics
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The battery is the most ideal power source of the twenty-first century, and has a bright future in many applications, such as portable consumer electronics, electric vehicles (EVs), military and aerospace systems, and power storage for renewable energy sources, because of its many advantages that make it the most promising technology. EVs are viewed as one of the novel solutions to land transport systems, as they reduce overdependence on fossil energy. With the current growth of EVs, it calls for innovative ways of supplementing EVs power, as overdependence on electric power may add to expensive loads on the power grid. However lithium-ion batteries (LIBs) for EVs have high capacity, and large serial/parallel numbers, when coupled with problems like safety, durability, uniformity, and cost imposes limitations on the wide application of lithium-ion batteries in EVs. These LIBs face a major challenge of battery life, which research has shown can be extended by cell balancing. The common areas under which these batteries operate with safety and reliability require the effective control and management of battery health systems. A great deal of research is being carried out to see that this technology does not lead to failure in the applications, as its failure may lead to catastrophes or lessen performance. This paper, through an analytical review of the literature, gives a brief introduction to battery management system (BMS), opportunities, and challenges, and provides a future research agenda on battery health management. With issues raised in this review paper, further exploration is essential.

ACS Style

Zachary Bosire Omariba; Lijun Zhang; Dongbai Sun. Review on Health Management System for Lithium-Ion Batteries of Electric Vehicles. Electronics 2018, 7, 72 .

AMA Style

Zachary Bosire Omariba, Lijun Zhang, Dongbai Sun. Review on Health Management System for Lithium-Ion Batteries of Electric Vehicles. Electronics. 2018; 7 (5):72.

Chicago/Turabian Style

Zachary Bosire Omariba; Lijun Zhang; Dongbai Sun. 2018. "Review on Health Management System for Lithium-Ion Batteries of Electric Vehicles." Electronics 7, no. 5: 72.

Journal article
Published: 18 April 2018 in Sensors
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Aircraft service process is in a state of the composite load of pressure and temperature for a long period of time, which inevitably affects the inherent characteristics of some components in aircraft accordingly. The flow field of aircraft wing materials under different Mach numbers is simulated by Fluent in order to extract pressure and temperature on the wing in this paper. To determine the effect of coupling stress on the wing’s material and structural properties, the fluid-structure interaction (FSI) method is used in ANSYS-Workbench to calculate the stress that is caused by pressure and temperature. Simulation analysis results show that with the increase of Mach number, the pressure and temperature on the wing’s surface both increase exponentially and thermal stress that is caused by temperature will be the main factor in the coupled stress. When compared with three kinds of materials, titanium alloy, aluminum alloy, and Haynes alloy, carbon fiber composite material has better performance in service at high speed, and natural frequency under coupling pre-stressing will get smaller.

ACS Style

Lijun Zhang; Changyan Sun. Simulation Analysis of Fluid-Structure Interaction of High Velocity Environment Influence on Aircraft Wing Materials under Different Mach Numbers. Sensors 2018, 18, 1248 .

AMA Style

Lijun Zhang, Changyan Sun. Simulation Analysis of Fluid-Structure Interaction of High Velocity Environment Influence on Aircraft Wing Materials under Different Mach Numbers. Sensors. 2018; 18 (4):1248.

Chicago/Turabian Style

Lijun Zhang; Changyan Sun. 2018. "Simulation Analysis of Fluid-Structure Interaction of High Velocity Environment Influence on Aircraft Wing Materials under Different Mach Numbers." Sensors 18, no. 4: 1248.

Journal article
Published: 03 April 2018 in Inorganica Chimica Acta
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A 2-hydroxy-1-naphthaldehyde-based chemosensor, 4-hydroxy-3-[(2-hydroxy-1-naphthyl)methylideneamino]benzoic acid (H2L) was synthesized and characterized by IR spectrum, elemental analysis, mass spectrum, 1H NMR and single crystal X-ray diffraction. Fluorescent spectra show that H2L can be used to detect Al3+ ions not only in ethanol, but also in DMF and DMSO. The enhancement of fluorescent intensity can be observed by naked-eye. And the detection limit can reach 10−9 M. The Job’s plot, 1H NMR titration, and electrospray ionization mass spectrometry (ESI-MS) results show that Al3+ ions and H2L form a 2:1 complex. Further study shows that this detection was reversible and ESIPT process can be used to explain the sensing mechanism.

ACS Style

Changyan Sun; Xiao Miao; Lijun Zhang; Wenjun Li; Zhidong Chang. Design and synthesis of a 2-hydroxy-1-naphthaldehyde -based fluorescent chemosensor for selective detection of aluminium ion. Inorganica Chimica Acta 2018, 478, 112 -117.

AMA Style

Changyan Sun, Xiao Miao, Lijun Zhang, Wenjun Li, Zhidong Chang. Design and synthesis of a 2-hydroxy-1-naphthaldehyde -based fluorescent chemosensor for selective detection of aluminium ion. Inorganica Chimica Acta. 2018; 478 ():112-117.

Chicago/Turabian Style

Changyan Sun; Xiao Miao; Lijun Zhang; Wenjun Li; Zhidong Chang. 2018. "Design and synthesis of a 2-hydroxy-1-naphthaldehyde -based fluorescent chemosensor for selective detection of aluminium ion." Inorganica Chimica Acta 478, no. : 112-117.

Research article
Published: 26 March 2018 in Mathematical Problems in Engineering
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The failure data of bearing products is random and discrete and shows evident uncertainty. Is it accurate and reliable to use Weibull distribution to represent the failure model of product? The Weibull distribution, log-normal distribution, and an improved maximum entropy probability distribution were compared and analyzed to find an optimum and precise reliability analysis model. By utilizing computer simulation technology and k-s hypothesis testing, the feasibility of three models was verified, and the reliability of different models obtained via practical bearing failure data was compared and analyzed. The research indicates that the reliability model of two-parameter Weibull distribution does not apply to all situations, and sometimes, two-parameter log-normal distribution model is more precise and feasible; compared to three-parameter log-normal distribution model, the three-parameter Weibull distribution manifests better accuracy but still does not apply to all cases, while the novel proposed model of improved maximum entropy probability distribution fits not only all kinds of known distributions but also poor information issues with unknown probability distribution, prior information, or trends, so it is an ideal reliability analysis model with least error at present.

ACS Style

Xia Xintao; Chang Zhen; Zhang Lijun; Xintao Xia. Estimation on Reliability Models of Bearing Failure Data. Mathematical Problems in Engineering 2018, 2018, 1 -21.

AMA Style

Xia Xintao, Chang Zhen, Zhang Lijun, Xintao Xia. Estimation on Reliability Models of Bearing Failure Data. Mathematical Problems in Engineering. 2018; 2018 ():1-21.

Chicago/Turabian Style

Xia Xintao; Chang Zhen; Zhang Lijun; Xintao Xia. 2018. "Estimation on Reliability Models of Bearing Failure Data." Mathematical Problems in Engineering 2018, no. : 1-21.

Journal article
Published: 16 March 2018 in IEEE Access
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As the secondary widely used battery, lithium-ion batteries (LIBs) have become the core component of the energy supply for most devices. Accurately predicting the current cycle time of LIBs is of great importance to ensure the reliability and safety of the equipment. In this paper, considering the nonlinear and non-Gaussian capacity degradation characteristics of LIBs, a remaining useful life (RUL) prediction method based on the exponential model and the particle filter is proposed. The cycle life test data of LIBs published by prognostics center of excellence in national aeronautics and space administration were exponentially experiencing the rule of degradation. And then the extrapolation method was used to get the quantitative expression of the uncertainty of life expectancy of LIBs, i.e. the prediction mean and the probability distribution histogram. The prognostic horizon index and the new specific accuracy index were applied to evaluate the prediction performance. Moreover, the prediction error under different prediction starting points is given. Compared with other methods such as the auto-regressive integrated moving average model, the fusion nonlinear degradation autoregressive model and the regularized particle filter algorithm, the proposed algorithm has a better prediction performance. According to the accuracy index, the proposed prediction method has better prediction accuracy and convergence. The RUL prediction for LIBs can provide a better decision support for the maintenance and support systems to optimize maintenance strategies, and reduce maintenance costs.

ACS Style

Lijun Zhang; Zhongqiang Mu; Changyan Sun. Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Exponential Model and Particle Filter. IEEE Access 2018, 6, 17729 -17740.

AMA Style

Lijun Zhang, Zhongqiang Mu, Changyan Sun. Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Exponential Model and Particle Filter. IEEE Access. 2018; 6 ():17729-17740.

Chicago/Turabian Style

Lijun Zhang; Zhongqiang Mu; Changyan Sun. 2018. "Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Exponential Model and Particle Filter." IEEE Access 6, no. : 17729-17740.

Journal article
Published: 11 February 2018 in Algorithms
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Aiming at the pitting fault of deep groove ball bearing during service, this paper uses the vibration signal of five different states of deep groove ball bearing and extracts the relevant features, then uses a neural network to model the degradation for identifying and classifying the fault type. By comparing the effects of training samples with different capacities through performance indexes such as the accuracy and convergence speed, it is proven that an increase in the sample size can improve the performance of the model. Based on the polynomial fitting principle and Pearson correlation coefficient, fusion features based on the skewness index are proposed, and the performance improvement of the model after incorporating the fusion features is also validated. A comparison of the performance of the support vector machine (SVM) model and the neural network model on this dataset is given. The research shows that neural networks have more potential for complex and high-volume datasets.

ACS Style

Lijun Zhang; Junyu Tao. Research on Degeneration Model of Neural Network for Deep Groove Ball Bearing Based on Feature Fusion. Algorithms 2018, 11, 21 .

AMA Style

Lijun Zhang, Junyu Tao. Research on Degeneration Model of Neural Network for Deep Groove Ball Bearing Based on Feature Fusion. Algorithms. 2018; 11 (2):21.

Chicago/Turabian Style

Lijun Zhang; Junyu Tao. 2018. "Research on Degeneration Model of Neural Network for Deep Groove Ball Bearing Based on Feature Fusion." Algorithms 11, no. 2: 21.

Journal article
Published: 28 September 2017 in Applied Sciences
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Equivalent circuit models are a hot research topic in the field of lithium-ion batteries for electric vehicles, and scholars have proposed a variety of equivalent circuit models, from simple to complex. On one hand, a simple model cannot simulate the dynamic characteristics of batteries; on the other hand, it is difficult to apply a complex model to a real-time system. At present, there are few systematic comparative studies on equivalent circuit models of lithium-ion batteries. The representative first-order resistor-capacitor (RC) model and second-order RC model commonly used in the literature are studied comparatively in this paper. Firstly, the parameters of the two models are identified experimentally; secondly, the simulation model is built in Matlab/Simulink environment, and finally the output precision of these two models is verified by the actual data. The results show that in the constant current condition, the maximum error of the first-order RC model is 1.65% and the maximum error for the second-order RC model is 1.22%. In urban dynamometer driving schedule (UDDS) condition, the maximum error of the first-order RC model is 1.88%, and for the second-order RC model the maximum error is 1.69%. This is of great instructional significance to the application in practical battery management systems for the equivalent circuit model of lithium-ion batteries of electric vehicles.

ACS Style

Lijun Zhang; Hui Peng; Zhansheng Ning; Zhongqiang Mu; Changyan Sun. Comparative Research on RC Equivalent Circuit Models for Lithium-Ion Batteries of Electric Vehicles. Applied Sciences 2017, 7, 1002 .

AMA Style

Lijun Zhang, Hui Peng, Zhansheng Ning, Zhongqiang Mu, Changyan Sun. Comparative Research on RC Equivalent Circuit Models for Lithium-Ion Batteries of Electric Vehicles. Applied Sciences. 2017; 7 (10):1002.

Chicago/Turabian Style

Lijun Zhang; Hui Peng; Zhansheng Ning; Zhongqiang Mu; Changyan Sun. 2017. "Comparative Research on RC Equivalent Circuit Models for Lithium-Ion Batteries of Electric Vehicles." Applied Sciences 7, no. 10: 1002.

Journal article
Published: 07 August 2017 in Applied Sciences
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Lithium-ion batteries are increasingly used in mobile applications where mechanical vibrations and shocks are a constant companion. There is evidence both in the academic and industrial communities to suggest that the electrical performance and mechanical properties of the lithium-ion cells of an electric vehicle (EV) are affected by the road-induced vibration. However, only a few studies related to the effects of vibration on the degradation of electrical performance of lithium-ion batteries have been approached. Therefore, this paper aimed to investigate the effects of vibration on the DC resistance, 1C capacity and consistency of NCR18650BE lithium-ion cells. Based on mathematical statistics, the method changes of the DC resistance and the capacity of the cells both before and after the test were analyzed with a large sample size. The results identified that a significant increase in DC resistance was observed as a result of vibration at the 95% confidence level, while typically a reduction in 1C capacity was also noted. In addition, based on a multi-feature quantity, a clustering algorithm was adopted to analyze the effect of vibration on cell consistency; the results show that the cell consistency had deteriorated after the vibration test.

ACS Style

Lijun Zhang; Zhansheng Ning; Hui Peng; Zhongqiang Mu; Changyan Sun. Effects of Vibration on the Electrical Performance of Lithium-Ion Cells Based on Mathematical Statistics. Applied Sciences 2017, 7, 802 .

AMA Style

Lijun Zhang, Zhansheng Ning, Hui Peng, Zhongqiang Mu, Changyan Sun. Effects of Vibration on the Electrical Performance of Lithium-Ion Cells Based on Mathematical Statistics. Applied Sciences. 2017; 7 (8):802.

Chicago/Turabian Style

Lijun Zhang; Zhansheng Ning; Hui Peng; Zhongqiang Mu; Changyan Sun. 2017. "Effects of Vibration on the Electrical Performance of Lithium-Ion Cells Based on Mathematical Statistics." Applied Sciences 7, no. 8: 802.

Journal article
Published: 16 October 2014 in Entropy
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Performance degradation assessment of rolling element bearings is vital for the reliable and cost-efficient operation and maintenance of rotating machines, especially for the implementation of condition-based maintenance (CBM). For robust degradation assessment of rolling element bearings, uncertainties such as those induced from usage variations or sensor errors must be taken into account. This paper presents an information exergy index for bearing performance degradation assessment that combines singular value decomposition (SVD) and the information exergy method. Information exergy integrates condition monitoring information of multiple instants and multiple sensors, and thus performance degradation assessment uncertainties are reduced and robust degradation assessment results can be obtained using the proposed index. The effectiveness and robustness of the proposed information exergy index are validated through experimental case studies.

ACS Style

Bin Zhang; Lijun Zhang; Jinwu Xu; Pingfeng Wang. Performance Degradation Assessment of Rolling Element Bearings Based on an Index Combining SVD and Information Exergy. Entropy 2014, 16, 5400 -5415.

AMA Style

Bin Zhang, Lijun Zhang, Jinwu Xu, Pingfeng Wang. Performance Degradation Assessment of Rolling Element Bearings Based on an Index Combining SVD and Information Exergy. Entropy. 2014; 16 (10):5400-5415.

Chicago/Turabian Style

Bin Zhang; Lijun Zhang; Jinwu Xu; Pingfeng Wang. 2014. "Performance Degradation Assessment of Rolling Element Bearings Based on an Index Combining SVD and Information Exergy." Entropy 16, no. 10: 5400-5415.

Journal article
Published: 30 April 2008 in Mechanical Systems and Signal Processing
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ACS Style

Lijun Zhang; Jinwu Xu; Jianhong Yang; Debin Yang; Dadong Wang. Multiscale morphology analysis and its application to fault diagnosis. Mechanical Systems and Signal Processing 2008, 22, 597 -610.

AMA Style

Lijun Zhang, Jinwu Xu, Jianhong Yang, Debin Yang, Dadong Wang. Multiscale morphology analysis and its application to fault diagnosis. Mechanical Systems and Signal Processing. 2008; 22 (3):597-610.

Chicago/Turabian Style

Lijun Zhang; Jinwu Xu; Jianhong Yang; Debin Yang; Dadong Wang. 2008. "Multiscale morphology analysis and its application to fault diagnosis." Mechanical Systems and Signal Processing 22, no. 3: 597-610.