ORCID Profile
0000-0002-7265-0008
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Manufacturing Safety and Quality | Mechanical Engineering | Numerical Modelling and Mechanical Characterisation
Publisher: Elsevier BV
Date: 02-2018
Publisher: American Scientific Publishers
Date: 04-2017
Abstract: Numerical modeling has been recognized as the dispensable tools for mechanical fault mechanism analysis. Techniques, ranging from macro to nano levels, include the finite element modeling boundary element modeling, modular dynamic modeling, nano dynamic modeling and so forth. This work firstly reviewed the progress on the fault mechanism analysis for gear transmissions from the tribological and dynamic aspects. Literature review indicates that the tribological and dynamic properties were separately investigated to explore the fault mechanism in gear transmissions. However, very limited work has been done to address the links between the tribological and dynamic properties and scarce researches have been done for coal cutting machines. For this reason, the tribo-dynamic coupled model was introduced to bridge the gap between the tribological and dynamic models in fault mechanism analysis for gear transmissions in coal cutting machines. The modular dynamic modeling and nano dynamic modeling techniques are expected to establish the links between the tribological and dynamic models. Possible future research directions using the tribo dynamic coupled model were summarized to provide potential references for researchers in the field.
Publisher: SAGE Publications
Date: 22-04-2014
Abstract: High effective management of civil aircraft spare parts is of significant importance for the economical operation of aircrafts. However, the stochastic characteristics of the aircraft spare parts make it difficult to find a reliable rule to precisely predict the future demand. In order to address this issue, this work presents a novel multi-components accumulation and high resolution analysis (MCAHR) method to improve the forecasting performance of aircraft spare parts. The MCAHR takes the advantages of high resolution of the wavelet transform to analyze the time series of spare part intermittent demand. The original time series were decomposed into several sub-bands along with the time axis of the wavelet. Then particle swarm optimized fuzzy neural networks were established for each sub-band to intelligently mine their intrinsic features. Accurate prediction result was hence obtained by the accumulation of the outputs of all fuzzy neural networks. Experimental tests using the historical data of A320 civil aircrafts were carried out in this work to evaluate the proposed MCAHR method. The analysis results have demonstrated a high efficiency of the MCAHR method and that its prediction performance is superior to existing methods. Hence, the proposed MCAHR method has practical importance in the civil aircraft spare part intermittent demand prediction and will provide a significant economic benefit to the industry through reasonable management of aircraft spare parts.
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: IOP Publishing
Date: 24-04-2019
Publisher: IOP Publishing
Date: 25-01-2017
Publisher: Elsevier BV
Date: 10-2011
Publisher: British Institute of Non-Destructive Testing (BINDT)
Date: 09-2016
Publisher: American Chemical Society (ACS)
Date: 28-12-2020
DOI: 10.26434/CHEMRXIV.13484763.V1
Abstract: CAMKK2 is a serine/threonine kinase and an activator of AMPK whose dysregulation is linked with multiple diseases. Unfortunately, STO-609, the tool inhibitor commonly used to probe CAMKK2 signaling, has limitations. To identify promising scaffolds as starting points for the development of high-quality CAMKK2 chemical probes, we utilized a hinge-binding scaffold hopping strategy to design new CAMKK2 inhibitors. Starting from the potent but promiscuous disubstituted 7-azaindole GSK650934 (CAMKK2 IC50 = 3 nM), a total of 32 compounds, composed of single ring, 5,6-, and 6,6-fused heteroaromatic cores were synthesized. The compound set was specifically designed to probe interactions with the kinase hinge-binding residues. These compounds were evaluated in vitro in biochemical and cellular assays for CAMKK2 inhibition. Compared to GSK650394 and STO-609, thirteen of our compounds displayed similar or better CAMKK2 inhibitory potency in vitro, while compounds 13g and 45 had greatly improved selectivity for CAMKK2 across the kinome. Our systematic survey of hinge binding chemotypes identified several potent and selective inhibitors of CAMKK2 to serve as starting points for medicinal chemistry programs aimed at the identification of CAMKK2 chemical probes and clinical candidates
Publisher: Elsevier BV
Date: 12-2017
Publisher: Elsevier BV
Date: 2011
Publisher: SPIE
Date: 07-08-2010
DOI: 10.1117/12.866387
Publisher: SAGE Publications
Date: 18-05-2012
Abstract: New general ships powered by electric propulsion always operate at low motor speeds. Popular sensorless vector control methods, including open loop and closed loop methods, are not reliable in this application. This is because the magnitude of back-electromotive force voltage is very small in the low speed region, and the performance of these methods is therefore significantly degraded. To overcome this problem, a high-frequency injection method is introduced in the control of the ship electric propulsion system. The injected periodic signal creates a high-frequency revolving field to act as a carrier. Then the desired information about the position angle of the motor can be retrieved. A simulation model of a ship electric propulsion system is established in Matlab/Simulink. The test results show that the newly proposed control system works stably with various ship operating conditions and robustly against speed variations. Its estimated position error is less than 0.3 rad. Moreover, the proposed sensorless vector control approach has been compared with the closed-loop-based model reference adaptive system. The comparison has demonstrated that the dynamic characteristics of the proposed control system are superior to the latter.
Publisher: American Society of Mechanical Engineers
Date: 08-06-2014
Abstract: Since there is an evident tendency of development of large scale ships, the interaction between the propulsion shaft and ship hull becomes severe due to the tremendously increased ship size. As a result the reliability of the vessels has been put in an important position by the companies and the governments all over the world. The excited forces caused by severe sea waves have considerable effects on the hull deformation which could have further impact on the shaft propulsion system. This paper aims to investigate the coupling dynamics between the large ship propulsion system and hull subjected by sea wave in 2-dimensional circumstance. To look into the coupling mechanism between the ship propulsion shaft, hull and sea waves, a 2-dimensional novel model of large ship propulsion-hull coupling system is presented in this work to analyze the dynamic interactions of the ship propulsion system and hull. According to the dynamic equations of the coupling model, the dynamical responses of the ship shaft and hull are obtained under different stiffness of the support bearings. The analysis indicates that choosing the suitable stiffness of bearings have an important effect on the coupled system.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2020
Publisher: MDPI AG
Date: 19-05-2018
Publisher: IOP Publishing
Date: 15-11-2019
Publisher: Elsevier BV
Date: 08-2016
Publisher: Springer Science and Business Media LLC
Date: 20-11-2015
Publisher: Elsevier BV
Date: 08-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2020
Publisher: Elsevier BV
Date: 08-2022
Publisher: Springer Science and Business Media LLC
Date: 02-11-2015
Publisher: SAGE Publications
Date: 20-11-2014
Abstract: Reliability and safety issues on marine diesel engines have been received and still need considerable attentions. The literature review indicates that a large amount of failures are caused by abnormal wear of the diesel engine components. It is therefore essential to monitor the engine condition using the tribological information. To further promote the oil monitoring technology into industrial application, a new on-line condition monitoring and remote fault diagnosis system for marine diesel engines is proposed in this article. The new system consists of an on-line tribological signal acquisition model in the ship, a remote feature extraction model and a fault diagnosis model in the laboratory center. The third-generation telecommunication (3G/B3G) which is called wireless communication system is adopted to connect the remote ship with laboratory center. Nine wear characteristics are extracted to detect the engine faults. Experimental tests are implemented on a diesel engine in a real ship named “Changjing 2” to evaluate the performance of the proposed fault diagnosis system. The results show that index of particle covered area can be treated as the best feature for marine diesel engines fault detection. The results also reflect that the new system offers satisfactory on-line fault diagnosis ability and is effective for the diesel engine fault diagnosis in practice.
Publisher: Elsevier BV
Date: 02-2018
Publisher: SAGE Publications
Date: 11-2010
DOI: 10.1260/0957-4565.41.10.29
Abstract: Rotor systems have been extensively used in a variety of industrial applications. However an unexpected failure may cause a break down of the rotational machinery, resulting in production and significant economic losses. Efficient incipient fault diagnosis is therefore critical to the machinery normal operation. Noise and vibration analysis is popular and effective for the rotor fault diagnosis. One of the key procedures in the fault diagnosis is feature extraction and selection. Literature review indicates that only limited research considered the nonlinear property of the feature space by the use of manifold learning algorithms in the field of mechanical fault diagnosis, and nonlinear feature extraction for rotor multi-fault detection has not been studied. This paper reports a new development based on a novel supervised manifold learning algorithm (adaptive locally linear embedding) applied to nonlinear feature extraction for rotor multiple defects identification. The adaptive locally linear embedding (ALLE) combines with the adaptive nearest neighbour algorithm and supervised locally linear embedding (LLE) to provide an adaptive supervised learning. Hence, distinct nonlinear features could be extracted from high-dimensional dataset effectively. Based on ALLE, a new fault diagnosis approach has been proposed. The independent component analysis (ICA) was firstly employed to separate the faulty components of the rotor vibration from the observation data. Then wavelet transform (WT) was used to decompose the recovered signals, and statistical features of frequency bands were hence calculated. Lastly, ALLE was applied to learn the low-dimensional intrinsic structure of the original feature space. The experiments on vibration data of single and coupled rotor faults have demonstrated that sensitive fault features can be extracted efficiently after the ICA-WT-ALLE processing, and the proposed diagnostic system is effective for the multi-fault identification of the rotor system. Furthermore, the proposed method achieves higher performance in terms of the classification rate than other feature extraction methods such as principal component analysis (PCA) and locally linear embedding (LLE).
Publisher: Elsevier BV
Date: 02-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: American Society of Mechanical Engineers
Date: 06-08-2017
Abstract: The objective of this research is to introduce a new ensemble prognostics method with degradation-dependent weights. Specifically, this method assigns an optimized, degradation-dependent weight to each learner (i.e., learning algorithm) such that the weighted sum of the prediction results from all the learners predicts the RUL of mechanical components with better accuracy. The ensemble prognostic algorithm is demonstrated using a data set collected from an engine simulator. Analysis results show that the predictive model trained by the ensemble learning algorithm outperform the existing methods.
Publisher: British Institute of Non-Destructive Testing (BINDT)
Date: 11-2013
Publisher: Springer Science and Business Media LLC
Date: 06-05-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-11-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: IOP Publishing
Date: 03-09-2019
Abstract: With unceasing increase of mining depth and development intensity, mining disasters such as rock burst have been increasing frequently, which often result in catastrophic accidents. Therefore, it is imperative to accurately forecast underground disasters. Previous research has suggested that the combination of drill-hole pressure relief and acoustic emission (AE) monitoring serves as an effective measure method towards the forecasting and prevention of disastrous accidents. However, the AE evolution mechanism of underground rock damages remains a challenge more specifically, the relationships among the drilling hole positions, depths and diameters, and the stress–strain and AE characteristics of the rocks are discussed little in the literature. In order to bridge this research gap, the particle flow code (PFC2D) is employed to systemically investigate the hidden patterns among the mechanical properties, AE and damage evolution of the rock mass with different positions, depths and diameters of the drilling holes. Analysis results demonstrate that the drilling position influences the rock stress–strain and AE characteristics in the plastic deformation stage and the residual stage while the hole depth affects the drilling process. More specifically, the initial AE strength, AE impact at the peak moment, AE fluctuations and induction time are significantly influenced by the drilling position and depth. Furthermore, the drilling position and depth change the evolution law in the damage acceleration and stable development stages, while the hole diameter has little effect on the AE signal during the rock drilling process.
Publisher: Elsevier BV
Date: 2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2016
Publisher: Institute of Electronics, Information and Communications Engineers (IEICE)
Date: 2012
DOI: 10.1587/ELEX.9.1260
Publisher: IGI Global
Date: 2013
DOI: 10.4018/978-1-4666-2095-7.CH015
Abstract: Marine power machinery parts are key equipments in ships. Ships always work in rigorous conditions such as offshore, heavy load, et cetera. Therefore, the failures in marine power machinery would badly threaten the safety of voyages. Keeping marine power machineries running reliably is the guarantee of voyage safety. For the condition monitoring and fault diagnosis of marine power machinery system, this study established the systemic condition identification approach for the tribo-system of marine power machinery and developed integrated diagnosis method by combining on-line and off-line ways for marine power machinery. Lastly, the remote fault diagnosis system was developed for practical application in marine power machinery, which consists of monitoring system in the ship, diagnosis system in laboratory centre, and maintenance management & maintenance decision support system.
Publisher: Informa UK Limited
Date: 10-11-2016
Publisher: IOP Publishing
Date: 04-02-2020
Abstract: This paper aims to develop a surrogate model for dynamics analysis of a magnetorheological d er (MRD) in the semi-active seat suspension system. An improved fruit fly optimization algorithm (IFOA) which enhances the global search capability of the original FOA is proposed to optimize the structure of a back propagation neural network (BPNN) in establishing the surrogate model. An MRD platform was fabricated to generate experimental data to feed the IFOA-BPNN model. Intrinsic patterns about the MRD dynamics behind the datasets have been discovered to establish a reliable MRD surrogate model. The outputs of the surrogate model demonstrate satisfactory dynamics characteristics in consistence with the experimental results. Moreover, the performance of the IFOA-BPNN based surrogate model was compared with that produced by the BPNN based, genetic algorithm-BPNN based, and FOA-BPNN based surrogate models. The comparison result shows better tracking capacity of the proposed method on the hysteresis behaviors of the MRD. As a result, the newly developed surrogate model can be used as the basis for advanced controller design of the semi-active seat suspension system.
Publisher: IOP Publishing
Date: 07-04-2020
Publisher: Elsevier BV
Date: 2018
Publisher: Kaunas University of Technology (KTU)
Date: 03-2012
Publisher: Kaunas University of Technology (KTU)
Date: 12-10-2015
Publisher: British Institute of Non-Destructive Testing (BINDT)
Date: 03-2013
Publisher: Elsevier BV
Date: 04-2019
Publisher: Springer Science and Business Media LLC
Date: 28-10-2015
Publisher: Springer Netherlands
Date: 2012
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: SAGE Publications
Date: 03-11-2016
Abstract: During the operation process of a gearbox, the vibration signals can reflect the dynamic states of the gearbox. The feature extraction of the vibration signal will directly influence the accuracy and effectiveness of fault diagnosis. One major challenge associated with the extraction process is the mode mixing, especially under such circumstance of intensive frequency. A novel fault diagnosis method based on frequency-modulated empirical mode decomposition is proposed in this paper. Firstly, several stationary intrinsic mode functions can be obtained after the initial vibration signal is processed using frequency-modulated empirical mode decomposition method. Using the method, the vibration signal feature can be extracted in unworkable region of the empirical mode decomposition. The method has the ability to separate such close frequency components, which overcomes the major drawback of the conventional methods. Numerical simulation results showed the validity of the developed signal processing method. Secondly, energy entropy was calculated to reflect the changes in vibration signals in relation to faults. At last, the energy distribution could serve as eigenvector of support vector machine to recognize the dynamic state and fault type of the gearbox. The analysis results from the gearbox signals demonstrate the effectiveness and veracity of the diagnosis approach.
Publisher: Elsevier BV
Date: 06-2016
Publisher: Elsevier BV
Date: 03-2017
Publisher: Elsevier BV
Date: 2023
Publisher: Springer Science and Business Media LLC
Date: 30-01-2018
Publisher: Elsevier BV
Date: 08-2016
Publisher: Informa UK Limited
Date: 30-03-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: IOP Publishing
Date: 29-08-2019
Publisher: MDPI AG
Date: 27-04-2022
DOI: 10.3390/MI13050684
Abstract: The flavoring process ensures the quality of cigarettes by endowing them with special tastes. In this process, the flavoring liquid is atomized into particles by a nozzle and mixed with the tobacco in a rotating drum. The particle size of the flavoring liquid has great influence on the atomization effect however, limited research has addressed the quantitation of the liquid particle size in two-phase nozzle flow. To bridge this research gap, the authors of this study employed numerical and experimental techniques to explore the quantitative analysis of particle size. First, a simulation model for the flavoring nozzle was established to investigate the atomization effect under different ejection pressures. Then, an experimental test is carried out to compare the test results with the simulation results. Lastly, the influencing factors of liquid particle size in two-phase nozzle flow were analyzed to quantify particle size. The analysis results demonstrated that there was a cubic correction relationship between the simulation and experiment particle size. The findings of this study may provide a reliable reference when evaluating the atomization effect of flavoring nozzles.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2022
Publisher: IOP Publishing
Date: 12-05-2016
Publisher: SAGE Publications
Date: 26-12-2017
Abstract: This work attempts to introduce a new intelligent method for condition monitoring of diesel engines. Diesel engine is one of the most important power providers for various industrial applications, including automobiles, ships, agricultures, construction, and electrical machinery. Due to harsh working environment, diesel engines are vulnerable to failures. This article addresses a significant need to improve predictive maintenance activities in diesel engines. A new failure diagnostics approach was proposed based on the manifold learning and swarm intelligence optimized multiclass multi-kernel relevant vector machine. Three manifold learning algorithms were first respectively used to fuse the features that extracted from the original vibration data of the diesel engines into a new nonlinear space. The fused features contain the most distinct health information of the engine by discarding redundant features. Then, the swarm intelligence optimized multiclass multi-kernel relevant vector machine was proposed to identify the failures using the fused features. The contribution of this research is that the dragonfly algorithm is employed to optimize the weights of the multi-kernel functions in the multiclass relevant vector machine. It was also applied to establishing a weighted-sum model by combining the outputs of swarm intelligence optimized multiclass multi-kernel relevant vector machine models with different manifold learning algorithms. Robust failure detection of diesel engines is achieved owing to combined strengths of multiple kernel functions and weighted-sum strategy. The effectiveness of the proposed method is demonstrated by experimental vibration data collected from a commercial diesel engine. The failure detection capability of the proposed manifold learning and swarm intelligence optimized multiclass multi-kernel relevant vector machine method for diesel engines will potentially benefit the machine condition monitoring industry by improving budgeting/forecasting and/or enabling just-in-time maintenance.
Publisher: SAGE Publications
Date: 11-2010
DOI: 10.1260/0957-4565.41.10.76
Abstract: Gear mechanisms are an important element in a variety of industrial applications and about 80% of the breakdowns of the transmission machinery are caused by the gear failure. Efficient incipient fault detection and accurate fault diagnosis are therefore critical to machinery normal operation. A new hybrid intelligent diagnosis method is proposed in this work to identify multiple categories of gear defection. In this method, wavelet packet transform (WPT), empirical mode decomposition (EMD) and Wigner-Ville distributions (WVD), combined with autoregressive (AR) model algorithm, were performed on gear vibration signals to extract useful fault characteristic information. Then, multidimensional feature sets including energy distribution, statistical features and AR parameters were obtained to represent gear operation conditions from different perspectives. The nonlinear dimensionality reduction algorithm, i.e. isometric mapping (Isomap), was employed in statistics to mine the intrinsic structure of the feature space in a low-dimensional space, and thus to speed up the training of the probabilistic neural network (PNN) classifier and enhance its diagnosis accuracy. Experiments with different gear faults were conducted, and the vibration signals were measured under different drive speeds and loads. The analysis results indicate that the proposed method is feasible and effective in the gear multi-fault diagnosis, and the isolation of different gear conditions, including normal, single crack, compound fault of wear and spalling, etc., has been accomplished. Since the recognition results are available directly from the output of PNN, the proposed diagnosis technique provides the possibility to fulfill the automatic recognition on gear multiple faults
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: American Society of Mechanical Engineers
Date: 06-08-2017
Abstract: Accurate modeling of the electrical behavior of a lithiumion (Li-ion) battery can provide accurate dynamic characteristics of the battery during charging/discharging and relaxation phases, which is essential to accurate online estimation of the battery state of charge (SoC). This paper proposes an ensemble bias-correction (BC) method with adaptive weights to improve the accuracy of an equivalent circuit model (ECM) in dynamic modeling of Li-ion batteries. The contribution of this paper is twofold: (i) the development of a novel ensemble method based on BC learning to model the dynamic characteristics of Li-ion batteries and (ii) the creation of an adaptive-weighting scheme to learn online the weights of offline member BC models for building an online ensemble BC model. Repeated pulsing tests with single and multiple C-rates were conducted on seven Li-ion battery cells to evaluate the effectiveness of the proposed ensemble BC method. The analysis results with the use of an ECM demonstrate that the proposed method can reduce, on average, the voltage modeling error of the ECM by at least 50%.
Publisher: Springer Science and Business Media LLC
Date: 03-2011
Publisher: Elsevier BV
Date: 2011
Publisher: Kaunas University of Technology (KTU)
Date: 07-09-2012
Publisher: Elsevier BV
Date: 2013
Publisher: IEEE
Date: 2010
DOI: 10.1109/MVHI.2010.86
Publisher: Mechanical Engineering Faculty in Slavonski Brod
Date: 2015
Publisher: IOP Publishing
Date: 12-08-2022
Abstract: The velocity sensitive characteristic of the conventional linear magnetorheological (MR) d er is undesirable in the application of impact protection. It will induce large d ing forces when the d er suffers high velocity impacts, whilst comprising the energy dissipation efficiency of the d er and posing a serious threat to occupants and mechanical structures. This work reports a new MR impact d er (NMRID) with low velocity sensitivity. Unlike the conventional MR impact d er (CMRID) in which MR fluids (MRFs) flow from one chamber to the other through a small annular gap between the piston and cylinder, the NMRID has a whole annular gap between the shaft and cylinder that is filled with MRFs, and the MRFs work in a pure shear mode without any liquid flow. In this work, a NMRID and a CMRID were prototyped. The velocity sensitivities of these two impact d ers were compared via numerical analysis and experimental impact tests. The analysis and test results indicate that NMRID possesses a much lower velocity sensitivity than the CMRID the dynamic range of the NMRID decreases less than CMRID with the increase of nominal impact velocity. Then, to demonstrate the controllability of NMRID, impact tests with a bang–bang control were implemented, and the peak force of NMRID was successfully controlled around a target force under different levels of nominal impact velocity. This research proves that the designed NMRID is less sensitive to velocity than the CMRID and the NMRID has good controllability, demonstrating that the NMRID can serve as a better candidate than CMRID in applications with high impact velocity.
Publisher: American Chemical Society (ACS)
Date: 28-12-2020
DOI: 10.26434/CHEMRXIV.13484763
Abstract: CAMKK2 is a serine/threonine kinase and an activator of AMPK whose dysregulation is linked with multiple diseases. Unfortunately, STO-609, the tool inhibitor commonly used to probe CAMKK2 signaling, has limitations. To identify promising scaffolds as starting points for the development of high-quality CAMKK2 chemical probes, we utilized a hinge-binding scaffold hopping strategy to design new CAMKK2 inhibitors. Starting from the potent but promiscuous disubstituted 7-azaindole GSK650934 (CAMKK2 IC50 = 3 nM), a total of 32 compounds, composed of single ring, 5,6-, and 6,6-fused heteroaromatic cores were synthesized. The compound set was specifically designed to probe interactions with the kinase hinge-binding residues. These compounds were evaluated in vitro in biochemical and cellular assays for CAMKK2 inhibition. Compared to GSK650394 and STO-609, thirteen of our compounds displayed similar or better CAMKK2 inhibitory potency in vitro, while compounds 13g and 45 had greatly improved selectivity for CAMKK2 across the kinome. Our systematic survey of hinge binding chemotypes identified several potent and selective inhibitors of CAMKK2 to serve as starting points for medicinal chemistry programs aimed at the identification of CAMKK2 chemical probes and clinical candidates br /
Publisher: Mechanical Engineering Faculty in Slavonski Brod
Date: 2015
Publisher: IEEE
Date: 03-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2020
Publisher: American Chemical Society (ACS)
Date: 15-07-2021
Publisher: IEEE
Date: 03-2010
Publisher: IOP Publishing
Date: 22-01-2021
Abstract: Spherical magnetorheological fluid (MRF) robots are capable to move in narrow space, which can be used for drug releasing to human stomach however, the magnetic-controlled rolling movement often generates a large displacement error, which greatly hinders the practical applications of the MRF robots. In order to bridge this research gap, this paper introduces a new MRF robot with a precise locomotion controller. In this control system, a data acquisition system is designed for the MRF robot and an optimal proportion integration differentiation (PID) controller is proposed based on an improved grey wolf optimization algorithm (IGWO). Both simulations and experiments have been performed to verify the performance of the locomotion controller. The simulation results show that the proposed IGWO-PID controller is superior to the conventional PID and GWO PID controller, with faster response output and smaller overshoot. Experimental analysis results demonstrate the proposed MRF robot can move in a complex trace with a speed fluctuation rate below 5.4%. As a result, precise locomotion has been achieved to make the new MRF robot ready for medicine delivery in narrow space.
Publisher: IOP Publishing
Date: 15-12-2017
Publisher: Kaunas University of Technology (KTU)
Date: 18-03-2014
Publisher: Springer Science and Business Media LLC
Date: 08-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Publisher: Elsevier BV
Date: 12-2017
No related organisations have been discovered for Zhixiong Li.
Start Date: 05-2019
End Date: 04-2021
Amount: $325,000.00
Funder: Australian Research Council
View Funded Activity