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.

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The electrostatic method is a rapidly growing research field in the monitoring of rotating equipment. This article reports how a specially-designed integrated electrostatic sensor was utilized to monitor the working conditions of rolling bearings. We firstly analyzed the principle and mathematic modeling of inner current noise generated by cable vibration and deformation. To ensure the electrostatic monitoring avoided being affected by cable vibration and deformation, we designed a ring-shaped printed circuit board to integrate the electrostatic electrode and condition unit together, which can be mounted tightly beside the rolling bearings; in this way, the cable connecting the electrode with the condition unit is removed and the distance between the electrode and rollers is shortened. Experiments using this integrated sensor board were conducted to obtain the electrostatic signals of working bearings. Time features were analyzed in detail to show the differences of four working bearings across the whole process. Experimental results show that the proposed integrated electrostatic sensor has a good performance in the electrostatic monitoring of rolling bearings.

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.

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