In industrial production processes, rotational speed is a key parameter for equipment condition monitoring and fault diagnosis. To achieve rotational speed measurement of rotational equipment under a condition of high temperature and heavy dust, this article proposes a digital approach using an electrostatic sensor. The proposed method utilizes a strip of a predetermined material stuck on the rotational shaft which will accumulate a charge because of the relative motion with the air. Then an electrostatic sensor mounted near the strip is employed to obtain the fluctuating signal related to the rotation of the charged strip. Via a signal conversion circuit, a square wave, the frequency of which equals that of the rotation shaft can be obtained. Having the square wave, the M/T method and T method are adopted to work out the rotational speed. Experiments were conducted on a laboratory-scale test rig to compare the proposed method with the auto-correlation method. The largest relative errors of the auto-correlation method with the sampling rate of 2 ksps, 5 ksps are 3.2% and 1.3%, respectively. The relative errors using digital approaches are both within ±4‰. The linearity of the digital approach combined with the M/T method or T method is also superior to that of the auto-correlation method. The performance of the standard deviations and response speed was also compared and analyzed to show the priority of the digital approach.

This paper presents a sectional water fraction measurement method using electrical capacitance tomography (ECT) image. To achieve a desired measurement accuracy, an optimal threshold-search method based on fast simulated annealing (FSA) algorithm is applied for image binarization; and to guarantee the imaging time consumption, fast fixed point continuation (FFPC) iterative algorithm cooperating with the compressed sensing (CS) theory was applied to the image reconstruction. In this study, the imaging time consumption and the measurement accuracy of water fraction are used to compare the proposed method and other methods. A numerical model is established with COMSOL Multiphysics to conduct the simulative validation of the proposed method firstly and physical experiments are conducted then. The experiment results show the average relative error of water fraction is lower than 15%, where the even as low as 3.68%.

Electrical capacitance tomography (ECT) is an image-based technology which has been widely used for gas-solid two-phase flow measurement. Solid phase electrification is an inevitable phenomenon during pneumatic conveying. To reduce the influence of medium electrification on ECT image reconstruction, a composite sensitivity matrix (CSM) which takes into account the effect of charge electrification is developed in this paper, so that image reconstruction error can be well suppressed. This paper firstly analyzes how medium electrification affects ECT sensitivity field from the perspective of electromagnetic theory. The CSM is then derived accordingly. Simulations and physical experiments are carried out to validate the proposed method. Experiment results show that the relative error and the correlative coefficient of the reconstructed images have been significantly improved comparing with that in traditional approaches. The results support effectiveness of the proposed method.

By applying an electrical capacitance sensor to sectional water fraction measurement for gas–water (salinity) two-phase flow, this paper investigates the effect of a medium’s conductivity on the measurement of capacitance sensors and its elimination. Firstly, the mechanism of how a medium’s conductivity influences the capacitance measurement of a capacitance sensor is analyzed by combining a physical model of the sensor with its conditioning circuit. The theoretical analysis indicates that the applied exciting frequency is a key factor influencing the conductivity effect; the effect can be restrained by increasing the excitation frequency. Then, an exciting frequency optimization rule is proposed accordingly. In addition, sensor configuration and geometry optimization (based on finite element method simulations) are discussed to achieve better performance. Dynamic and static calibration experiments are carried out for stratified gas–liquid (salinity) two-phase flow for the purpose of validating the proposed method. The experimental results confirm the effectiveness of the proposed method. This work is constructive to the design of capacitive sensing technologies applied to conductive objects, and offers insights into the structural design and optimization of capacitance sensors.

Electrical capacitance tomography (ECT) is a promising imaging technology of permittivity distributions in multiphase flow. To reduce the effect of charging phenomenon on ECT measurement, an improved extreme learning machine method combined with adaptive soft-thresholding (AST-ELM) is presented and studied for image reconstruction. This method can provide a nonlinear mapping model between the capacitance values and medium distributions by using machine learning but not an electromagnetic-sensitive mechanism. Both simulation and experimental tests are carried out to validate the performance of the presented method, and reconstructed images are evaluated by relative error and correlation coefficient. The results have illustrated that the image reconstruction accuracy by the proposed AST-ELM method has greatly improved than that by the conventional methods under the condition with charging object.

In this paper, the electrical method combines the electrostatic sensor and capacitance sensor to measure the phase concentration of pulverized coal/biomass/air three-phase flow through data fusion technology. In order to eliminate the effects of flow regimes and improve the accuracy of the phase concentration measurement, the mel frequency cepstrum coefficient features extracted from electrostatic signals are used to train the Continuous Gaussian Mixture Hidden Markov Model (CGHMM) for flow regime identification. Support Vector Machine (SVM) is introduced to establish the concentration information fusion model under identified flow regimes. The CGHMM models and SVM models are transplanted on digital signal processing (DSP) to realize on-line accurate measurement. The DSP flow regime identification time is 1.4 ms, and the concentration predict time is 164 μs, which can fully meet the real-time requirement. The average absolute value of the relative error of the pulverized coal is about 1.5% and that of the biomass is about 2.2%.