About

Lijuan Wang received her BEng degree in Computer Science and Technology from Qiqihar University, Heilongjiang, China in 2010 and her PhD degree in Measurement and Automation from North China Electric Power University (NCEPU), Beijing, China in 2014. She subsequently obtained a second PhD degree in Electronic Engineering from the University of Kent, Canterbury, UK in 2017. After that she worked at the University of Teesside as a Lecturer in Instrumentation and Control Engineering. Since June 2018 she has been a Lecturer in Electronic Engineering at the University of Kent.

She was awarded the Postgraduate National Scholarship in 2014 and Excellent PhD Thesis in 2015 by NCEPU, the Best Student Poster in 2014 I2MTC (International Instrumentation and Measurement Technology Conference), IEEE Graduate Fellowship by IEEE Instrumentation and Measurement Society in 2015 and Best Presentation Award in 2015 ISMTMF (International Symposium on Measurement Techniques for Multiphase Flows).

Her main areas of expertise include electrostatic sensing, multiphase flow measurement, condition monitoring of mechanical systems, sensors and instrumentation systems, data analysis and soft computing.

Research interests

Her research interests include electrostatic sensing, multiphase flow measurement, condition monitoring of mechanical systems, sensors and instrumentation systems, data analysis and soft computing.

Teaching

Current Teaching Commitments

  • EL305 Introduction to Electronics
  • EL315 Digital Technologies

Previous Teaching Commitments

  • Fundamentals of Mathematics

Supervision

PhD research topics

  •  Condition Monitoring of Wind Turbines through Multispectral Imaging
  • Vibration Measurement of Rotor-bearing Systems Using Electrostatic Sensors
  • Gas-solid Flow Measurement in Fluidized Beds through Multi-modal Sensing and Deep Learning
  • Molten Salt Metering in Solar Power Stations through Multi-modal Sensing and Data Modelling
  • CO2 Flow Metering under CCS Conditions Using Coriolis Mass Flowmeters and Soft Computing Techniques

Professional

Prizes and Awards

  • 2015 Best Presentation Award, International Symposium on Measurement Techniques For Multiphase Flows, Sapporo, Japan.
  • 2015 IEEE Graduate Fellowship Award, IEEE Instrumentation and Measurement Society.
  • 2014 Best Student Poster Award (1st Place), IEEE International Instrumentation and Measurement Technology Conference, Montevideo, Uruguay.
  • 2014 Student Travel Award, IEEE International Instrumentation and Measurement Technology Conference, Montevideo, Uruguay.

Publications

Article

  • Yan, Y. et al. (2018). Application of Soft Computing Techniques to Multiphase Flow Measurement: A Review. Flow Measurement and Instrumentation [Online] 60:30-43. Available at: https://doi.org/10.1016/j.flowmeasinst.2018.02.017.
    After extensive research and development over the past three decades, a range of techniques have been proposed and developed for online continuous measurement of multiphase flow. In recent years, with the rapid development of computer hardware and machine learning, soft computing techniques have been applied in many engineering disciplines, including indirect measurement of multiphase flow. This paper presents a comprehensive review of the soft computing techniques for multiphase flow metering with a particular focus on the measurement of individual phase flowrates and phase fractions. The paper describes the sensors used and the working principle, modelling and example applications of various soft computing techniques in addition to their merits and limitations. Trends and future developments of soft computing techniques in the field of multiphase flow measurement are also discussed.
  • Wang, T. et al. (2018). Rotational Speed Measurement through Image Similarity Evaluation and Spectral Analysis. IEEE Access [Online]:1-12. Available at: https://doi.org/10.1109/ACCESS.2018.2866479.
    Accurate and reliable measurement of rotational speed is desirable in a variety of industries.
    This paper presents a rotational speed measurement system based on a low-cost imaging device with a
    simple marker on the rotor. Sequential images are pre-processed through denoising, histogram equalization
    and circle Hough transform, and then processed by similarity evaluation methods to obtain the similarity
    level of images. Finally, the rotational speed is obtained through Chirp-Z transform on the restructured
    signals. The measurement principle, structure design and performance assessment of the proposed system
    are presented. The effects of different influence factors, including frame rate, marker shape and size,
    algorithm for image similarity evaluation, illumination conditions, shooting angle and photographic
    distance, on the performance of the measurement system are quantified and discussed through a series of
    experimental tests on a laboratory test rig. Experimental results suggest that the system is capable of
    providing constant rotational speed measurement with a maximum relative error of ±0.6% and a
    repeatability of less than 0.6% over a speed range from 100 to 900 RPM. Under varying speed conditions
    the proposed system can achieve valid measurement with a relative error within ±1% over the speed range
    of 300 to 900 RPM.
  • Hu, Y. et al. (2017). On-line Continuous Measurement of the Operating Deflection Shape of Power Transmission Belts Through Electrostatic Charge Sensing. IEEE Transactions on Instrumentation and Measurement.
    The measurement of the operating deflection shape (ODS) of power transmission belts is of great importance for the fault diagnosis and prognosis of industrial belt drive systems. This paper presents a novel method based on an electrostatic sensor array to measure the ODS of a belt moving both axially and transversely. The electrostatic sensor integrates a charge amplifier that converts the induced charge on a strip-shaped electrode into a voltage signal. Finite element simulations are performed to study the sensing characteristics of the sensor and the results reveal that the sensor can respond to vibration displacement. Construction of the ODS is achieved in the frequency domain using the ODS frequency response function. Comparative experimental studies with a high-accuracy laser displacement sensor were conducted on a purpose-built test rig and the results show that the vibration frequencies and their relative magnitudes obtained from both sensors agree well with each other. Experiments conducted over a range of belt axial speeds show that the belt vibrates at frequencies that are well separated and identifiable using a peak picking method. The measured ODSs of the first three vibration modes illustrate that the vibration displacement is larger in the middle of the belt span than at both ends and that the phase shift relative to the reference sensor at each measurement point increases monotonically along the belt running direction. The belt axial speed determines the vibration frequencies and displacement, which reaches the maximum amplitude around the natural frequency of the belt.
    Index Terms – Belt drive; vibration measurement; electrostatic sensor; charge amplifier; sensor array; operating deflection shape; frequency response function.
  • Wang, L., Yan, Y. and Reda, K. (2017). Comparison of Single and Double Electrostatic Sensors for Rotational Speed Measurement. Sensors and Actuators A: Physical [Online] 266:46-55. Available at: http://dx.doi.org/10.1016/j.sna.2017.09.014.
    Accurate and reliable measurement of rotational speed is crucial in many industrial processes. Recent research provides an alternative approach to rotational speed measurement of dielectric rotors through electrostatic sensing and signal processing. This paper aims to explore the electrostatic phenomenon of rotational machineries, design considerations of the spacing between double electrostatic sensors and effect of dielectric rotors on the performance of the rotational speed measurement systems based on single and double electrostatic sensors. Through a series of experimental tests with rotors of different material types, including polytetrafluoroethylene (PTFE), polyvinyl chloride (PVC) and Nylon, different surface roughness (Ra 3.2 and Ra 6.3) and difference diameters (60 mm and 120 mm), the accuracy and reliability of the two measurement systems are assessed and compared. Experimental results suggest that more electrostatic charge is generated on the PTFE rotors with a larger diameter and coarser surface and hence better performance of the measurement systems. The single-sensor system yields a relative error of within ±1% while the double-sensor system produces an error within ±1.5% over the speed range of 500 - 3000 rpm for all tested rotors. However, the single-sensor system outperforms the double-sensor system at high rotational speeds (>2000 rpm) with a relative error less than ±0.05%.
  • Wang, L. et al. (2017). Mass Flow Measurement of Gas-Liquid Two-Phase CO2 in CCS Transportation Pipelines using Coriolis Flowmeters. International Journal of Greenhouse Gas Control [Online] 68:269-275. Available at: https://doi.org/10.1016/j.ijggc.2017.11.021.
    Carbon Capture and Storage (CCS) is a promising technology that stops the release of CO2 from industrial processes such as electrical power generation. Accurate measurement of CO2 flows in a CCS system where CO2 flow is a gas, liquid, or gas-liquid two-phase mixture is essential for the fiscal purpose and potential leakage detection. This paper presents a novel method based on Coriolis mass flowmeters in conjunction with least squares support vector machine (LSSVM) models to measure gas-liquid two-phase CO2 flow under CCS conditions. The method uses a classifier to identify the flow pattern and individual LSSVM models for the metering of CO2 mass flowrate and prediction of gas volume fraction of CO2, respectively. Experimental work was undertaken on a multiphase CO2 flow test facility. Performance comparisons between the general LSSVM and flow pattern based LSSVM models are conducted. Results demonstrate that Coriolis mass flowmeters with the LSSVM model incorporating flow pattern identification algorithms perform significantly better than those using the general LSSVM model. The mass flowrate measurement of gas-liquid CO2 is found to yield errors less than ±2% on the horizontal pipeline and ±1.5% on the vertical pipeline, respectively, over flowrates from 250 kg/h to 3200 kg/h. The error in the estimation of CO2 gas volume fraction is within ±10% over the same range of flow rates.
  • Hu, Y. et al. (2017). A Smart Electrostatic Sensor for On-line Condition Monitoring of Power Transmission Belts. IEEE Transactions on Industrial Electronics [Online] 64:7313-7322. Available at: http://dx.doi.org/10.1109/TIE.2017.2696507.
    On-line condition monitoring of power transmission belts is essential to keep industrial belt-driven equipment functioning smoothly and reliably. This paper presents a smart electrostatic sensor that monitors belt motion through detection of static charge on the belt. A theoretical model is established using the method of moments for calculation of induced charge on strip-shaped electrodes placed in the vicinity of a belt moving both axially and transversely. The sensor unit converts the induced charge into proportional voltage signals using charge amplifiers and measures belt speed and vibration through cross correlation and spectral analysis, respectively. The performance of the smart electrostatic sensor is validated against a photoelectric rotary encoder and a laser displacement sensor. Comparative experimental results show that the belt speed can be measured with a relative error within ±2% over the range 1.7-15.5 m/s. The electrostatic sensor is capable of measuring the frequencies of transverse vibration accurately. Although absolute displacement cannot be measured due to the uncertain level of charge on the belt, the measurement results of relative vibration magnitudes for different modes and at different belt speeds are reasonably accurate.
  • Wang, L. et al. (2017). Input variable selection for data-driven models of Coriolis flowmeters for two-phase flow measurement. Measurement Science & Technology [Online] 28:35305. Available at: https://doi.org/10.1088/1361-6501/aa57d6.
    Input variable selection is an essential step in the development of data-driven models for environmental, biological and industrial applications. Through input variable selection to eliminate the irrelevant or redundant variables, a suitable subset of variables is identified as the input of a model. Meanwhile, through input variable selection the complexity of the model structure is simplified and the computational efficiency is improved. This paper describes the procedures of the input variable selection for the data-driven models for the measurement of liquid mass flowrate and gas volume fraction under two-phase flow conditions using Coriolis flowmeters. Three advanced input variable selection methods, including Partial Mutual Information (PMI), Genetic Algorithm - Artificial Neural Network (GA-ANN) and tree-based Iterative Input Selection (IIS) are applied in this study. Typical data-driven models incorporating Support Vector Machine (SVM) are established individually based on the input candidates resulting from the selection methods. The validity of the selection outcomes is assessed through an output performance comparison of the SVM based data-driven models and sensitivity analysis. The validation and analysis results suggest that the input variables selected from the PMI algorithm provide more effective information for the models to measure liquid mass flowrate while the IIS algorithm provides a fewer but more effective variables for the models to predict gas volume fraction.
  • Wang, L. et al. (2016). Gas-liquid Two-phase Flow Measurement Using Coriolis Flowmeters Incorporating Artificial Neural Network, Support Vector Machine and Genetic Programming Algorithms. IEEE Transactions on Instrumentation and Measurement [Online]. Available at: https://doi.org/10.1109/TIM.2016.2634630.
    Coriolis flowmeters are well established for the mass flow measurement of single phase flow with high accuracy. In recent years attempts have been made to apply Coriolis flowmeters to measure two-phase flow. This paper presents data driven models that are incorporated in Coriolis flowmeters to measure both the liquid mass flowrate and the gas volume fraction of a two-phase flow mixture. Experimental work was conducted on a purpose-built two-phase flow test rig on both horizontal and vertical pipelines for a liquid mass flowrate ranging from 700 kg/h to 14500 kg/h and a gas volume fraction between 0 and 30%. Artificial Neural Network (ANN), Support Vector Machine (SVM) and Genetic Programming (GP) models are established through training with experimental data. The performance of BP-ANN (Back Propagation - ANN), RBF-ANN (Radial Basis Function - ANN), SVM and GP models is assessed and compared. Experimental results suggest that the SVM models are superior to the BP-ANN, RBF-ANN and GP models for two-phase flow measurement in terms of robustness and accuracy. For liquid mass flowrate measurement with the SVM models, 93.49% of the experimental data yield a relative error less than ±1% on the horizontal pipeline whilst 96.17% of the results are within ±1% on the vertical installation. The SVM models predict gas volume fraction with a relative error less than ±10% for 93.10% and 94.25% of the test conditions on horizontal and vertical installations, respectively.
  • Hu, Y. et al. (2016). Non-Contact Vibration Monitoring of Power Transmission Belts Through Electrostatic Sensing. IEEE Sensors Journal [Online] 16:3541-3550. Available at: http://doi.org/10.1109/JSEN.2016.2530159.
    On-line vibration monitoring plays an important role in the fault diagnosis and prognosis of industrial belt drive systems. This paper presents a novel measurement technique based on electrostatic sensing to monitor the transverse vibration of power transmission belts in an on-line, continuous, and non-contact manner. The measurement system works on the principle that variations in the distance between a strip-shaped electrode and the naturally electrified dielectric belt give rise to a fluctuating current output. The response of the sensor to a belt moving both axially and transversely is numerically calculated through finite-element modeling. Based on the sensing characteristics of the sensor, the transverse velocity of the belt is characterized through the spectral analysis of the sensor signal. Experiments were conducted on a two-pulley belt drive system to verify the validity of the sensing technique. The belt vibration at different axial speeds was measured and analyzed. The results show that the belt vibrates at well-separated modal frequencies that increase with the axial speed. A closer distance between the electrode and the belt makes higher order vibration modes identifiable, but also leads to severer signal distortion that produces higher order harmonics in the signal.















    On-line vibration monitoring plays an important role in the fault diagnosis and prognosis of industrial belt drive systems. This paper presents a novel measurement technique based on electrostatic sensing to monitor the transverse vibration of power transmission belts in an on-line, continuous, and non-contact manner. The measurement system works on the principle that variations in the distance between a strip-shaped electrode and the naturally electrified dielectric belt give rise to a fluctuating current output. The response of the sensor to a belt moving both axially and transversely is numerically calculated through finite-element modeling. Based on the sensing characteristics of the sensor, the transverse velocity of the belt is characterized through the spectral analysis of the sensor signal. Experiments were conducted on a two-pulley belt drive system to verify the validity of the sensing technique. The belt vibration at different axial speeds was measured and analyzed. The results show that the belt vibrates at well-separated modal frequencies that increase with the axial speed. A closer distance between the electrode and the belt makes higher order vibration modes identifiable, but also leads to severer signal distortion that produces higher order harmonics in the signal.
  • Hu, Y. et al. (2016). Simultaneous Measurement of Belt Speed and Vibration Through Electrostatic Sensing and Data Fusion. IEEE Transactions on Instrumentation and Measurement [Online] 65:1130-1138. Available at: http://doi.org/10.1109/TIM.2015.2490958.
    Accurate and reliable measurement of belt speed and vibration is of great importance in a range of industries. This paper presents a feasibility study of using an electrostatic sensor array and signal processing algorithms for the simultaneous measurement of belt speed and vibration in an online, continuous manner. The design, implementation, and assessment of an experimental system based on this concept are presented. In comparison with existing techniques, the electrostatic sensing method has the advantages of non-contact and simultaneous measurement, low cost, simple structure, and easy installation. The characteristics of electrostatic sensors are studied through finite-element modeling using a point charge moving in the sensing zone of the electrode. The sensor array is arranged in a 2 × 3 matrix, with the belt running between two rows of three identical sensing elements. The three signals in a row are cross correlated for speed calculation, and the results are then fused to give a final measurement. The vibration modes of the belt are identified by fusing the normalized spectra of vertically paired sensor signals. Experiments conducted on a two-pulley belt-driven rig show that the system can measure the belt speed with a relative error within ±2% over the range 2-10 m/s. More accurate and repeatable speed measurements are achieved for higher belt speeds and a shorter distance between the electrode and the belt. It is found that a stretched belt vibrates at the harmonics of the belt pass frequency and hence agrees the expected vibration characteristics.
  • Wang, L. et al. (2015). Rotational Speed Measurement Using Single and Dual Electrostatic Sensors. IEEE Sensors Journal [Online] 15:1784-1793. Available at: http://dx.doi.org/10.1109/JSEN.2014.2368091.
    Rotational speed is a key parameter for condition monitoring and control of rotating devices in many industrial processes. This paper presents a technique for the rotational speed measurement using a single or dual electrostatic sensors coupled with correlation signal processing algorithms. In the case of a single sensor, autocorrelation algorithm is applied to process the signal and measure the period of the rotational motion. In the case of dual sensors, cross correlation algorithm is applied to obtain the time delay between the two signals and hence the measurement of rotational speed. The fundamental characteristics of the sensing techniques with single or dual sensors for different sized rotors are studied through finite-element modeling. Experimental tests were conducted on a purpose-built test rig to assess the performance of both techniques over the speed range of 100-3000 r/min. The rotors used in the experimental tests are made of polyvinyl chloride with a diameter of 60 and 120 mm, respectively. Experimental results suggest that the measurement system using a single sensor is capable of producing repeatable rotational speed measurement with a maximum error of ±1.2% over the speed range of 600-3000 r/min. However, the measurement system with dual electrostatic sensors is capable of achieving valid measurements over a wider range of speeds (100-2000 r/min), although the measurement error is larger than that of the single-sensor system. Both techniques perform better with a larger rotor under a higher rotational speed.
  • Qian, X. et al. (2015). An integrated multi-channel electrostatic sensing and digital imaging system for the on-line measurement of biomass–coal particles in fuel injection pipelines. Fuel [Online] 151:2-10. Available at: http://dx.doi.org/10.1016/j.fuel.2014.11.013.
    The measurement of key parameters of biomass–coal particles in a pneumatic conveying pipeline at a power plant presents a significant challenge due to the inherent complexity of the dilute particle flow and differences in physical properties between the different kinds of fuels. This paper presents the latest development in on-line continuous measurement of mean particle velocity, concentration and particle size distribution of pulverised fuel using multi-channel electrostatic sensing and digital imaging techniques. An integrated instrumentation system has been implemented to achieve the intended measurement of pulverised fuel particles. Comprehensive tests were conducted on a 150 mm bore horizontal pipe section of a large scale test facility using pulverised coal and biomass–coal blends. The results suggest that the characteristics of the pulverized fuel flow depend on the flow velocity and biomass proportion in the mixture and, to a large extent, on the biomass properties. It is found that coal particles travel faster and carry more electrostatic charge than biomass–coal blends. As more biomass particles (up to 20% by weight) are added to the flow, the particle velocity reduces, the electrostatic charge level decreases, and the flow becomes less stable in comparison with coal flow.
  • Hu, Y. et al. (2015). On-line Sizing of Pneumatically Conveyed Particles Through Acoustic Emission Detection and Signal Analysis. IEEE Transactions on Instrumentation and Measurement [Online] 64:1100-1109. Available at: http://dx.doi.org/10.1109/TIM.2014.2355653.
    Accurate measurement of the particle size distribution of pneumatically conveyed pulverized fuel is critical to achieving optimal combustion efficiency and minimum pollutant emissions at coal and biomass-fired power plants. This paper presents a prototype instrumentation system for the on-line continuous measurement of particle size distribution through acoustic emission (AE) detection. The proposed method extracts particle size information from the impulsive AE signals arising from impacts of particles with a metallic waveguide protruding into the flow. The relationship between the particle size and the peak AE voltage is established through mathematical modeling of the particle impact process. Identification of the peak AE voltage is achieved with an energy-based peak detection algorithm. Experimental results obtained with glass beads demonstrate the capability of the system to discriminate particles of different sizes from the recorded AE signals. The system performs better in terms of accuracy for higher speed, lower concentration particles as the impact signals are stronger and better separated in the time domain.
  • Wang, L. et al. (2014). Rotational Speed Measurement Through Electrostatic Sensing and Correlation Signal Processing. IEEE Transactions on Instrumentation and Measurement [Online] 63:1190-1199. Available at: http://dx.doi.org/10.1109/TIM.2013.2292283.
    Rotational speed is a key parameter for the condition monitoring and control of rotating machineries, such as generators, electromotors, and centrifugal and machine tool spindles. It is essential for precision machining and early warning of faults to measure rotational speed in real time. This paper presents the principle and application of electrostatic sensors and correlation signal processing techniques to real-time measurement of rotational speed. The electrostatic sensors and signal conditioning and processing units were designed and implemented. Experimental tests were conducted on a laboratory-scale test rig under a range of conditions including different diameters of the shaft. The results obtained suggest that the distance between the electrodes and the surface of the rotating object is a key factor affecting the performance of the measurement system. The system performs better in terms of accuracy and repeatability at a higher rotational speed as more electrostatic charge is produced on the rotating surface. High and stable correlation coefficients acquired during the tests suggest that the measurement system is capable of providing reliable measurement of rotational speed under realistic industrial conditions.
  • Wang, L. and Yan, Y. (2014). Mathematical modelling and experimental validation of electrostatic sensors for rotational speed measurement. Measurement Science and Technology [Online] 25:115101. Available at: http://dx.doi.org/10.1088/0957-0233/25/11/115101.
    Recent research has demonstrated that electrostatic sensors can be applied to the measurement of rotational speed with excellent repeatability and accuracy under a range of conditions. However, the sensing mechanism and fundamental characteristics of the electrostatic sensors are still largely unknown and hence the design of the sensors is not optimised for rotational speed measurement. This paper presents the mathematical modelling of strip electrostatic sensors for rotational speed measurement and associated experimental studies for the validation of the modelling results. In the modelling, an ideal point charge on the surface of the rotating object is regarded as an impulse input to the sensing system. The fundamental characteristics of the sensor, including spatial sensitivity, spatial filtering length and signal bandwidth, are quantified from the developed model. The effects of the geometric dimensions of the electrode, the distance between the electrode and the rotor surface and the rotational speed being measured on the performance of the sensor are analyzed. A close agreement between the modelling results and experimental measurements has been observed under a range of conditions. Optimal design of the electrostatic sensor for a given rotor size is suggested and discussed in accordance with the modelling and experimental results.
  • Qian, X. et al. (2012). Quantitative characterization of pulverised coal and biomass-coal blends in pneumatic conveying pipelines using electrostatic sensor arrays and data fusion techniques. Measurement Science and Technology [Online] 23:1-13. Available at: https://doi.org/10.1088/0957-0233/23/8/085307.
    Quantitative data about the dynamic behaviour of pulverized coal and biomass–coal blends in fuel injection pipelines allow power plant operators to detect variations in fuel supply and oscillations in the flow at an early stage, enable them to balance fuel distribution between fuel feeding pipes and ultimately to achieve higher combustion efficiency and lower greenhouse gas emissions. Electrostatic sensor arrays and data fusion algorithms are combined to provide a non-intrusive solution to the measurement of fuel particle velocity, relative solid concentration and flow stability under pneumatic conveying conditions. Electrostatic sensor arrays with circular and arc-shaped electrodes are integrated in the same sensing head to measure 'averaged' and 'localized' characteristics of pulverized fuel flow. Data fusion techniques are applied to optimize and integrate the results from the sensor arrays. Experimental tests were conducted on the horizontal section of a 150 mm bore pneumatic conveyor circulating pulverized coal and sawdust under various flow conditions. Test results suggest that pure coal particles travel faster and carry more electrostatic charge than biomass–coal blends. As more biomass particles are added to the flow, the overall velocity of the flow reduces, the electrostatic charge level on particles decreases and the flow becomes less stable compared to the pure coal flow.

Conference or workshop item

  • Duan, Q. et al. (2018). Measurement of the Void Fraction of Gas-Liquid Two-Phase CO2 Flow Using Laser Attenuation Techniques. in: 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, pp. 1-5. Available at: https://doi.org/10.1109/I2MTC.2018.8409828.
    Carbon capture and storage (CCS) is a promising technology to reduce CO 2 emissions from industrial processes. However, void fraction measurement is one of the challenging issues to be solved for gas-liquid two-phase CO 2 flow measurement. This paper presents a novel measurement system using laser intensity attenuation techniques to measure the void fraction of two-phase CO 2 flow. The measurement system includes optical sensors, a laser detector array and a monolithic processor. The performance of the proposed measurement system is verified through experimental tests under various conditions, including stratified flow and bubbly flow. The void fraction of two-phase CO 2 flow ranges from 0 to 69%. Experimental results demonstrate that the system is capable of measuring the void fraction of CO 2 flow with an error between -2% and 3.6%.
  • Liu, J. et al. (2018). Investigations into the behaviours of Coriolis flowmeters under air-water two-phase flow conditions on an optimized experimental platform. in: 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, pp. 1-6. Available at: https://doi.org/10.1109/I2MTC.2018.8409681.
    Gas-liquid two-phase flow is commonly encountered in many industrial processes due to production requirement or inevitable gas entrainment from various sources. Accurate liquid phase measurement under two-phase conditions is challenging but important as it is the key factor to reduce cost, improve safety or meet legal requirements. Coriolis flowmeters, owing to their high accuracy in metering single-phase flow, direct mass flow measurement and multivariable sensing nature, are widely used in industry. Recently developed Coriolis flowmeters can work under multiphase conditions, making it possible to achieve accurate multiphase flow measurement through model based error compensation or training based soft computing correction. This paper assesses the behaviours of Coriolis flowmeters under various two-phase conditions for modelling and soft computing algorithm improvement, including previously investigated factors (flowrate, gas volume fraction, flow tube geometry, flow converter, and process pressure) and new factors (flow regimes in terms of bubble size and distribution). Experimental work was conducted on 25 mm and 50 mm bore air-water two-phase flow rigs for liquid mass flowrates between 2500 kg/h and 35000 kg/h with gas volume fraction of 0-60%. With the influence of each factor identified through univariate analysis, comparisons between existing modelling theories and experimental error curves are established. In the meantime, the rig design and control are optimized to provide efficient and automated data acquisition in order to supply ample and high-quality data for the training of soft computing models as well as enhancing the understanding in theoretical modelling.
  • Wang, L. et al. (2017). Mass flow measurement of two-phase carbon dioxide using Coriolis flowmeters. in: IEEE International Instrumentation and Measurement Technology Conference (I2MTC 2017).. Available at: http://dx.doi.org/10.1109/I2MTC.2017.7969891.
    Carbon Capture and Storage (CCS) is considered as an important technology to reduce CO2 emission from electrical power generation and other industrial processes. In the CCS chain, i.e. from capture to storage via transportation, it is essential to realize accurate measurement of CO2 flows for the purpose of accounting and potential leakage detection. However, there are some significant challenges for the current flow metering technologies to achieve the specified 1.5% measurement uncertainty in the EU-ETS (European Union - Emissions Trading Scheme) for all expected flow conditions. Moreover, there are very few CO2 flow test and calibration facilities that can recreate CCS conditions particularly two-phase CO2 flow in pipelines together with accurate measurement standards. As one of the most potential flowmeters that may be used in the CCS chain, Coriolis flowmeters have the advantages of direct measurement of mass flow rate regardless of its state (liquid, gas, gas/liquid two-phase or supercritical) in addition to the measurement of temperature and density of CO2 for the characterization of flow conditions. This paper assesses the performance of Coriolis flowmeters incorporating a soft-computing correction method for gas-liquid two-phase CO2 flow measurement. The correction method includes a pre-trained backpropagation neural network. Experimental work was conducted on a purpose-built 25 mm bore two-phase CO2 flow test rig for liquid mass flowrate between 300 kg/h and 3050 kg/h and gas mass flowrate from 0 to 330 kg/h under the fluid temperature of 19~21 °C and pressure of 54~58 bar. Experimental results suggest that the Coriolis flowmeters with the developed correction method are capable of providing the mass flow rate of gas-liquid CO2 flow with errors mostly within ±2% and ±1.5% on horizontal and vertical pipelines, respectively.
  • Wang, L. et al. (2016). Gas-liquid Two-phase Flow Measurement Using Coriolis Flowmeters Incorporating Neural Networks. in: IEEE International Instrumentation and Measurement Technology Conference (I2MTC 2016). IEEE, pp. 747-751. Available at: https://doi.org/10.1109/I2MTC.2016.7520458.
    Coriolis flowmeters are commonly used to measure single phase flow. In recent years attempts are being made to apply Coriolis flowmeters to measure two-phase flows. This paper presents a neural network based approach that has been applied to Coriolis flowmeters to measure both the liquid flow rate and the gas void fraction of a two-phase flow. Experimental tests were conducted on a purpose-built two-phase flow test rig on both horizontal and vertical pipelines. The mass flow rate ranges from 700 kg/h to 14500 kg/h whilst the gas volume fraction is between 0 and 30%. A set of variables, including observed density, apparent mass flow, pressure of the fluid and signals to maintain flow tube oscillation, are considered as inputs to a neural network. Two neural networks are established through training with experimental data obtained from the flow rig on horizontal and vertical pipelines, respectively. The performance of both neural networks is assessed in comparison with the reference readings. Experimental results suggest that the relative errors of the corrected mass flow rate of liquid for the vertical and horizontal installations are no greater than ±1.5% and ±2.5%, respectively. The gas volume fraction is predicted with relative errors of less than ±10% and ±20%, respectively, for vertical and horizontal installations in most cases.
  • Wang, L. et al. (2016). Radial Vibration Measurement of Rotary Shafts through Electrostatic Sensing and Hilbert-Huang Transform. in: IEEE International Instrumentation and Measurement Technology Conference (I2MTC 2016). IEEE, pp. 867-871. Available at: https://doi.org/10.1109/I2MTC.2016.7520480.
    Radial vibration measurement of rotary shafts plays a significant part in condition monitoring and fault diagnosis of rotating machinery. This paper presents a novel method for radial vibration measurement through electrostatic sensing and HHT (Hilbert-Huang Transform) signal processing. The foundational characteristics of the electrostatic sensor in the vicinity of a drifting shaft are studied through Finite Element Modelling. Experimental tests were conducted on a purpose-built test rig to characterize the operating condition of the rotor at different rotational speeds (400 rpm and 600 rpm). A normal working shaft and an eccentric shaft were tested and the output signals from the electrostatic sensors were analyzed. Through empirical mode decomposition (EMD) on the electrostatic signals, the intrinsic mode functions (IMF) including the vibration information of the shaft are identified and further analyzed in the time-frequency domain. Experimental results suggest that the electrostatic sensing technique in conjunction with HHT provides a simple and cost-effective approach to radial vibration measurement of rotary shafts.
  • Hu, Y. et al. (2016). On-line Continuous Measurement of the Operating Deflection Shape of Power Transmission Belts Through Electrostatic Sensing. in: IEEE International Instrumentation and Measurement Technology Conference (I2MTC 2016). IEEE, pp. 872-876. Available at: https://doi.org/10.1109/I2MTC.2016.7520481.
    The measurement of the operating deflection shape (ODS) of power transmission belts is of great importance for the fault diagnosis and prognosis of industrial belt drive systems. This paper presents a novel method based on an electrostatic sensor array to measure the ODS of a belt moving both axially and transversely. Finite element simulations are performed to study the sensing characteristics of a strip-shaped electrode and the results reveal that the transverse velocity determines the sensor signal. Construction of the ODS is achieved in the frequency domain using the ODS frequency response function. Experiments conducted on a purpose-built test rig show that the belt vibrates at resonant frequencies that are well separated and identifiable using a peak picking method. The ODSs for different vibration modes exhibit similar deformation patterns and the axial motion of the belt determines that the ODSs propagate along the belt length, rather than stay fixed in space.
  • Wang, L. et al. (2015). Effects of material type and surface roughness of the rotor on the electrostatic sensing based rotational speed measurement’. in: IEEE International Instrumentation and Measurement Technology Conference (I2MTC 2015),. pp. 452-457.
  • Hu, Y. et al. (2015). Simultaneous measurement of conveyor belt speed and vibration using an electrostatic sensor array’. in: IEEE International Instrumentation and Measurement Technology Conference (I2MTC 2015),. pp. 757-761.
  • Wang, L. et al. (2014). Performance assessment of the rotational speed measurement system based on a single electrostatic sensor. in: IEEE International Instrumentation and Measurement Technology Conference. pp. 135-138.
  • Hu, Y. et al. (2013). A miniature, low-cost MEMS AHRS with application to posture control of robotic fish. in: Instrumentation and Measurement Technology Conference (I2MTC), 2013 IEEE International. pp. 1392-1395. Available at: http://dx.doi.org/10.1109/I2MTC.2013.6555642.
    Inertial sensing is of paramount importance for a wide variety of navigation, guidance and control tasks. Historically, application of inertial sensing was limited to high-performance, high-cost aerospace and military fields. Recent MEMS technology has enabled miniaturization, mass production, and cost reduction of inertial sensors. This paper presents the development and robotic application of a miniature, low-cost AHRS unit based on MEMS technology. The sensor suite includes a tri-axis gyroscope, a tri-axis accelerometer and a tri-axis magnetometer. Fusion of the sensor measurements is achieved with a quaternion-based EKF algorithm. The performance of the AHRS unit is evaluated with a rotating platform and the results suggest numerous application possibilities. As an application case, the AHRS unit is employed for posture measurement of a robotic fish. By adjusting the lift forces produced by pectoral fins on both sides, the robotic fish can maintain desired posture during highspeed swimming
  • Wang, L. et al. (2013). Rotational speed measurement using electrostatic sensors and correlation signal processing techniques. in: Instrumentation and Measurement Technology Conference (I2MTC), 2013 IEEE International. pp. 224-227. Available at: http://dx.doi.org/10.1109/I2MTC.2013.6555413.
    Rotational speed is a key parameter for the condition monitoring and control of rotating equipment such as generators, electromotors, centrifugal and machine tool spindles. This paper presents the principle and application of electrostatic sensors and correlation signal processing techniques to real-time measurement of rotational speed. Experimental tests were conducted on a test rig under a range of conditions. The results suggest that the operability and accuracy of the measurement system depend on a number of factors, in particular, the distance between the electrodes and the surface of the rotating equipment. The system performs better under higher rotational speed conditions as more electrostatic charge is produced on the rotating surface.
  • Duan, Q. et al. (2013). Real-time apparent density measurement of the working fluid in outlet pipes of a steam-injection boiler. in: Instrumentation and Measurement Technology Conference (I2MTC), 2013 IEEE International. pp. 1844-1847. Available at: http://dx.doi.org/10.1109/I2MTC.2013.6555733.
    This paper presents a novel method for the real-time apparent density measurement of the working fluid in outlet pipes of a stream-injection boiler. The measurement system consists of a vertical measuring pipe, a container with constant height and a differential pressure (DP) transmitter. The working fluid in the upper part of the vertical measuring section flows into the container which is connected to the positive pressure side of the DP transmitter. The negative pressure side is connected to the lower part of the vertical measuring section through a pressleading tube. The apparent pressure measurement is then converted into DP measurement. When the feed water flow is known, the apparent density of the working fluid at the outlet is determined by the measured differential pressure. Results from oil field trials confirm that the proposed method is effective.
  • Wang, L. et al. (2013). Rotational speed measurement using electrostatic sensors with single or double electrodes. in: IET Renewable Power Generation Conference 2013.. Available at: http://dx.doi.org/10.1049/cp.2013.1736.
    Rotational speed is a key parameter for condition monitoring and control of rotating devices in the energy, power, transport and manufacturing industries. Especially in wind farms it is essential to monitor the operating performance of wind turbines in real-time to monitor its operating condition and detect potential fault at an early stage. This paper presents a novel rotational speed measurement technique using electrostatic sensors with single or double electrodes in conjunction with autocorrelation and cross-correlation algorithms. Preliminary investigations show the electrostatic charge accumulated on the surface of the rotating equipment due to friction between air and other media contains useful information about the operating condition of the rotating equipment. Electrostatic charge induced on the sensing electrodes adjoining to the rotating equipment can be detected with a suitable electronic signal processing circuit. The system consists of a sensing unit, signal conditioning unit, signal processing unit and system control unit. The rotational speed measurement system has been evaluated on a laboratory scale test rig under a range of conditions. The experimental data demonstrate that the electrostatic sensing technology has the advantages of high accuracy, robustness and resolution. Applicability and performance of the measurement system with single or double electrodes are discussed. The new technique has advantages of low cost, simple structure, easy installation and suitability for hostile environments over conventional methods.
  • Wang, L. et al. (2013). Intelligent condition monitoring of rotating machinery through electrostatic sensing and signal analysis. in: 2013 IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA),. pp. 1-4. Available at: http://dx.doi.org/10.1109/ICSIMA.2013.6717951.
    Condition monitoring is a key step to identify the health status of working machinery and establish a necessary maintenance strategy. This paper proposes a novel intelligent system for the online monitoring of the operating conditions of rotating machinery using electrostatic sensors and signal processing techniques. This system is capable of providing simultaneous measurements of rotational speed, angular acceleration, vibration direction and frequency as well as an indication of mechanical wear. These parameters usually contain abundant fault-related information about the rotating machinery, which is to be extracted by detecting the electrostatic charge on the surface of the moving part. The general principle and system design considerations are presented. Preliminary experimental results obtained from laboratory tests demonstrate the effectiveness of the monitoring system.
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